diff --git a/tutorials/README.md b/tutorials/README.md
index 63ac39aa..e6963a5c 100644
--- a/tutorials/README.md
+++ b/tutorials/README.md
@@ -17,21 +17,26 @@ Use this guide to navigate all tutorial tracks, understand structure rules, and
 <<<<<<< HEAD
 | Tutorial directories | 188 |
 | Tutorial markdown files | 1705 |
-| Tutorial markdown lines | 881,469 |
+| Tutorial markdown lines | 996,366 |
 =======
 <<<<<<< HEAD
 | Tutorial directories | 188 |
 | Tutorial markdown files | 1705 |
-| Tutorial markdown lines | 881,469 |
+| Tutorial markdown lines | 996,366 |
 =======
 <<<<<<< HEAD
 | Tutorial directories | 188 |
 | Tutorial markdown files | 1705 |
-| Tutorial markdown lines | 881,469 |
+| Tutorial markdown lines | 996,366 |
 =======
+<<<<<<< HEAD
 | Tutorial directories | 188 |
 | Tutorial markdown files | 1705 |
-| Tutorial markdown lines | 881,469 |
+| Tutorial markdown lines | 996,366 |
+=======
+| Tutorial directories | 188 |
+| Tutorial markdown files | 1705 |
+| Tutorial markdown lines | 996,366 |
 
 ## Source Verification Snapshot
 
@@ -50,6 +55,7 @@ Repository-source verification run against tutorial index references (GitHub API
 >>>>>>> origin/main
 >>>>>>> origin/main
 >>>>>>> origin/main
+>>>>>>> origin/main
 
 ## Content Structure Patterns
 
diff --git a/tutorials/openhands-tutorial/01-getting-started.md b/tutorials/openhands-tutorial/01-getting-started.md
index 3617ca37..4e81d92e 100644
--- a/tutorials/openhands-tutorial/01-getting-started.md
+++ b/tutorials/openhands-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: OpenHands Tutorial
 
 # Chapter 1: Getting Started with OpenHands
 
+Welcome to **Chapter 1: Getting Started with OpenHands**. In this part of **OpenHands Tutorial: Autonomous Software Engineering Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Install OpenHands, understand its architecture, and execute your first autonomous coding task.
 
 ## Overview
@@ -565,4 +568,152 @@ Next, we'll explore **basic operations** - file manipulation, command execution,
 
 **Ready for the next chapter?** [Chapter 2: Basic Operations](02-basic-operations.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenHands Tutorial: Autonomous Software Engineering Workflows**
+- tutorial slug: **openhands-tutorial**
+- chapter focus: **Chapter 1: Getting Started with OpenHands**
+- system context: **Openhands Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started with OpenHands`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenHands Repository](https://github.com/OpenHands/OpenHands)
+- [OpenHands Docs](https://docs.openhands.dev/)
+- [OpenHands Releases](https://github.com/OpenHands/OpenHands/releases)
+
+### Cross-Tutorial Connection Map
+
+- [OpenClaw Tutorial](../openclaw-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started with OpenHands`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `print`, `result`, `openhands` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with OpenHands` as an operating subsystem inside **OpenHands Tutorial: Autonomous Software Engineering Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `task`, `workspace`, `OpenHands` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with OpenHands` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `print`.
+2. **Input normalization**: shape incoming data so `result` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `openhands`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenHands Repository](https://github.com/OpenHands/OpenHands)
+  Why it matters: authoritative reference on `OpenHands Repository` (github.com).
+- [OpenHands Docs](https://docs.openhands.dev/)
+  Why it matters: authoritative reference on `OpenHands Docs` (docs.openhands.dev).
+- [OpenHands Releases](https://github.com/OpenHands/OpenHands/releases)
+  Why it matters: authoritative reference on `OpenHands Releases` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `print` and `result` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Basic Operations - Files, Commands, and Environments](02-basic-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openhands-tutorial/02-basic-operations.md b/tutorials/openhands-tutorial/02-basic-operations.md
index 86ab494f..f92c995c 100644
--- a/tutorials/openhands-tutorial/02-basic-operations.md
+++ b/tutorials/openhands-tutorial/02-basic-operations.md
@@ -8,6 +8,9 @@ parent: OpenHands Tutorial
 
 # Chapter 2: Basic Operations - Files, Commands, and Environments
 
+Welcome to **Chapter 2: Basic Operations - Files, Commands, and Environments**. In this part of **OpenHands Tutorial: Autonomous Software Engineering Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master file operations, command execution, environment management, and workspace navigation with OpenHands.
 
 ## Overview
@@ -601,4 +604,54 @@ Next, we'll explore **code generation** - OpenHands' ability to create high-qual
 
 **Ready for the next chapter?** [Chapter 3: Code Generation](03-code-generation.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `management`, `Create`, `OpenHands` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Basic Operations - Files, Commands, and Environments` as an operating subsystem inside **OpenHands Tutorial: Autonomous Software Engineering Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `environment`, `file`, `operations` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Basic Operations - Files, Commands, and Environments` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `management`.
+2. **Input normalization**: shape incoming data so `Create` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `OpenHands`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenHands Repository](https://github.com/OpenHands/OpenHands)
+  Why it matters: authoritative reference on `OpenHands Repository` (github.com).
+- [OpenHands Docs](https://docs.openhands.dev/)
+  Why it matters: authoritative reference on `OpenHands Docs` (docs.openhands.dev).
+- [OpenHands Releases](https://github.com/OpenHands/OpenHands/releases)
+  Why it matters: authoritative reference on `OpenHands Releases` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `management` and `Create` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with OpenHands](01-getting-started.md)
+- [Next Chapter: Chapter 3: Code Generation - Creating Production-Ready Code](03-code-generation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openhands-tutorial/03-code-generation.md b/tutorials/openhands-tutorial/03-code-generation.md
index 8560b5cc..3df4f836 100644
--- a/tutorials/openhands-tutorial/03-code-generation.md
+++ b/tutorials/openhands-tutorial/03-code-generation.md
@@ -8,6 +8,9 @@ parent: OpenHands Tutorial
 
 # Chapter 3: Code Generation - Creating Production-Ready Code
 
+Welcome to **Chapter 3: Code Generation - Creating Production-Ready Code**. In this part of **OpenHands Tutorial: Autonomous Software Engineering Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master OpenHands' code generation capabilities for functions, classes, complete applications, and production-ready systems.
 
 ## Overview
@@ -660,4 +663,54 @@ Next, we'll explore **bug fixing** - OpenHands' ability to identify, diagnose, a
 
 **Ready for the next chapter?** [Chapter 4: Bug Fixing](04-bug-fixing.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Include`, `testing`, `management` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Code Generation - Creating Production-Ready Code` as an operating subsystem inside **OpenHands Tutorial: Autonomous Software Engineering Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `generation`, `documentation`, `Create` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Code Generation - Creating Production-Ready Code` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Include`.
+2. **Input normalization**: shape incoming data so `testing` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `management`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenHands Repository](https://github.com/OpenHands/OpenHands)
+  Why it matters: authoritative reference on `OpenHands Repository` (github.com).
+- [OpenHands Docs](https://docs.openhands.dev/)
+  Why it matters: authoritative reference on `OpenHands Docs` (docs.openhands.dev).
+- [OpenHands Releases](https://github.com/OpenHands/OpenHands/releases)
+  Why it matters: authoritative reference on `OpenHands Releases` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Include` and `testing` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Basic Operations - Files, Commands, and Environments](02-basic-operations.md)
+- [Next Chapter: Chapter 4: Bug Fixing - Autonomous Debugging and Resolution](04-bug-fixing.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openhands-tutorial/04-bug-fixing.md b/tutorials/openhands-tutorial/04-bug-fixing.md
index 5c9e04fd..6ed754f0 100644
--- a/tutorials/openhands-tutorial/04-bug-fixing.md
+++ b/tutorials/openhands-tutorial/04-bug-fixing.md
@@ -8,6 +8,9 @@ parent: OpenHands Tutorial
 
 # Chapter 4: Bug Fixing - Autonomous Debugging and Resolution
 
+Welcome to **Chapter 4: Bug Fixing - Autonomous Debugging and Resolution**. In this part of **OpenHands Tutorial: Autonomous Software Engineering Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master OpenHands' systematic approach to identifying, diagnosing, and resolving code issues across multiple languages and frameworks.
 
 ## Overview
@@ -818,4 +821,54 @@ Next, we'll explore **testing** - OpenHands' ability to create comprehensive tes
 
 **Ready for the next chapter?** [Chapter 5: Testing](05-testing.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `issues`, `Issue`, `error` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Bug Fixing - Autonomous Debugging and Resolution` as an operating subsystem inside **OpenHands Tutorial: Autonomous Software Engineering Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `OpenHands`, `code`, `debugging` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Bug Fixing - Autonomous Debugging and Resolution` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `issues`.
+2. **Input normalization**: shape incoming data so `Issue` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `error`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenHands Repository](https://github.com/OpenHands/OpenHands)
+  Why it matters: authoritative reference on `OpenHands Repository` (github.com).
+- [OpenHands Docs](https://docs.openhands.dev/)
+  Why it matters: authoritative reference on `OpenHands Docs` (docs.openhands.dev).
+- [OpenHands Releases](https://github.com/OpenHands/OpenHands/releases)
+  Why it matters: authoritative reference on `OpenHands Releases` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `issues` and `Issue` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Code Generation - Creating Production-Ready Code](03-code-generation.md)
+- [Next Chapter: Chapter 5: Testing - Comprehensive Test Suite Generation and Quality Assurance](05-testing.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openhands-tutorial/05-testing.md b/tutorials/openhands-tutorial/05-testing.md
index 9096c6c6..11994269 100644
--- a/tutorials/openhands-tutorial/05-testing.md
+++ b/tutorials/openhands-tutorial/05-testing.md
@@ -8,6 +8,9 @@ parent: OpenHands Tutorial
 
 # Chapter 5: Testing - Comprehensive Test Suite Generation and Quality Assurance
 
+Welcome to **Chapter 5: Testing - Comprehensive Test Suite Generation and Quality Assurance**. In this part of **OpenHands Tutorial: Autonomous Software Engineering Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master OpenHands' testing capabilities for creating unit tests, integration tests, performance tests, and automated quality assurance.
 
 ## Overview
@@ -655,4 +658,54 @@ Next, we'll explore **refactoring** - OpenHands' ability to improve code structu
 
 **Ready for the next chapter?** [Chapter 6: Refactoring](06-refactoring.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `testing`, `Test`, `test` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Testing - Comprehensive Test Suite Generation and Quality Assurance` as an operating subsystem inside **OpenHands Tutorial: Autonomous Software Engineering Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Performance`, `integration`, `analysis` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Testing - Comprehensive Test Suite Generation and Quality Assurance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `testing`.
+2. **Input normalization**: shape incoming data so `Test` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `test`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenHands Repository](https://github.com/OpenHands/OpenHands)
+  Why it matters: authoritative reference on `OpenHands Repository` (github.com).
+- [OpenHands Docs](https://docs.openhands.dev/)
+  Why it matters: authoritative reference on `OpenHands Docs` (docs.openhands.dev).
+- [OpenHands Releases](https://github.com/OpenHands/OpenHands/releases)
+  Why it matters: authoritative reference on `OpenHands Releases` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `testing` and `Test` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Bug Fixing - Autonomous Debugging and Resolution](04-bug-fixing.md)
+- [Next Chapter: Chapter 6: Refactoring - Code Structure Improvement and Modernization](06-refactoring.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openhands-tutorial/06-refactoring.md b/tutorials/openhands-tutorial/06-refactoring.md
index c4546f57..ae240bf1 100644
--- a/tutorials/openhands-tutorial/06-refactoring.md
+++ b/tutorials/openhands-tutorial/06-refactoring.md
@@ -8,6 +8,9 @@ parent: OpenHands Tutorial
 
 # Chapter 6: Refactoring - Code Structure Improvement and Modernization
 
+Welcome to **Chapter 6: Refactoring - Code Structure Improvement and Modernization**. In this part of **OpenHands Tutorial: Autonomous Software Engineering Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master OpenHands' refactoring capabilities for improving code structure, performance, and maintainability through systematic code transformations.
 
 ## Overview
@@ -873,4 +876,54 @@ Next, we'll explore **integration** - OpenHands' ability to connect applications
 
 **Ready for the next chapter?** [Chapter 7: Integration](07-integration.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `refactoring`, `Refactor`, `Service` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Refactoring - Code Structure Improvement and Modernization` as an operating subsystem inside **OpenHands Tutorial: Autonomous Software Engineering Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `code`, `Code`, `OpenHands` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Refactoring - Code Structure Improvement and Modernization` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `refactoring`.
+2. **Input normalization**: shape incoming data so `Refactor` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Service`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenHands Repository](https://github.com/OpenHands/OpenHands)
+  Why it matters: authoritative reference on `OpenHands Repository` (github.com).
+- [OpenHands Docs](https://docs.openhands.dev/)
+  Why it matters: authoritative reference on `OpenHands Docs` (docs.openhands.dev).
+- [OpenHands Releases](https://github.com/OpenHands/OpenHands/releases)
+  Why it matters: authoritative reference on `OpenHands Releases` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `refactoring` and `Refactor` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Testing - Comprehensive Test Suite Generation and Quality Assurance](05-testing.md)
+- [Next Chapter: Chapter 7: Integration - Connecting Applications with External Services](07-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openhands-tutorial/07-integration.md b/tutorials/openhands-tutorial/07-integration.md
index bb3bee23..9ce67ffe 100644
--- a/tutorials/openhands-tutorial/07-integration.md
+++ b/tutorials/openhands-tutorial/07-integration.md
@@ -8,6 +8,9 @@ parent: OpenHands Tutorial
 
 # Chapter 7: Integration - Connecting Applications with External Services
 
+Welcome to **Chapter 7: Integration - Connecting Applications with External Services**. In this part of **OpenHands Tutorial: Autonomous Software Engineering Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master OpenHands' integration capabilities for connecting applications with APIs, databases, third-party services, and complex system architectures.
 
 ## Overview
@@ -680,4 +683,54 @@ Next, we'll explore **advanced projects** - building complete applications, micr
 
 **Ready for the next chapter?** [Chapter 8: Advanced Projects](08-advanced-projects.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `integration`, `processing`, `Event` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Integration - Connecting Applications with External Services` as an operating subsystem inside **OpenHands Tutorial: Autonomous Software Engineering Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Enhance`, `Include`, `service` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Integration - Connecting Applications with External Services` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `integration`.
+2. **Input normalization**: shape incoming data so `processing` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Event`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenHands Repository](https://github.com/OpenHands/OpenHands)
+  Why it matters: authoritative reference on `OpenHands Repository` (github.com).
+- [OpenHands Docs](https://docs.openhands.dev/)
+  Why it matters: authoritative reference on `OpenHands Docs` (docs.openhands.dev).
+- [OpenHands Releases](https://github.com/OpenHands/OpenHands/releases)
+  Why it matters: authoritative reference on `OpenHands Releases` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `integration` and `processing` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Refactoring - Code Structure Improvement and Modernization](06-refactoring.md)
+- [Next Chapter: Chapter 8: Advanced Projects - Complete Applications and System Architectures](08-advanced-projects.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openhands-tutorial/08-advanced-projects.md b/tutorials/openhands-tutorial/08-advanced-projects.md
index 03d5b342..6bce5855 100644
--- a/tutorials/openhands-tutorial/08-advanced-projects.md
+++ b/tutorials/openhands-tutorial/08-advanced-projects.md
@@ -8,6 +8,9 @@ parent: OpenHands Tutorial
 
 # Chapter 8: Advanced Projects - Complete Applications and System Architectures
 
+Welcome to **Chapter 8: Advanced Projects - Complete Applications and System Architectures**. In this part of **OpenHands Tutorial: Autonomous Software Engineering Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Build production-ready applications and complex system architectures with OpenHands, from microservices to full-stack platforms.
 
 ## Overview
@@ -704,4 +707,53 @@ This concludes our comprehensive OpenHands tutorial. You've learned how to lever
 
 *Congratulations! You've completed the comprehensive OpenHands Tutorial. You now have the skills to build production-ready applications and complex system architectures using autonomous AI software engineering.*
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `management`, `Service`, `time` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Advanced Projects - Complete Applications and System Architectures` as an operating subsystem inside **OpenHands Tutorial: Autonomous Software Engineering Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Real`, `platform`, `Content` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Advanced Projects - Complete Applications and System Architectures` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `management`.
+2. **Input normalization**: shape incoming data so `Service` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `time`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenHands Repository](https://github.com/OpenHands/OpenHands)
+  Why it matters: authoritative reference on `OpenHands Repository` (github.com).
+- [OpenHands Docs](https://docs.openhands.dev/)
+  Why it matters: authoritative reference on `OpenHands Docs` (docs.openhands.dev).
+- [OpenHands Releases](https://github.com/OpenHands/OpenHands/releases)
+  Why it matters: authoritative reference on `OpenHands Releases` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `management` and `Service` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Integration - Connecting Applications with External Services](07-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openskills-tutorial/01-getting-started.md b/tutorials/openskills-tutorial/01-getting-started.md
index 781d4fb3..4a7bd469 100644
--- a/tutorials/openskills-tutorial/01-getting-started.md
+++ b/tutorials/openskills-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: OpenSkills Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets OpenSkills installed and synchronizing skills into your agent environment.
 
 ## Quick Start
@@ -27,3 +30,607 @@ npx openskills sync
 You now have OpenSkills running with a synced baseline skill set.
 
 Next: [Chapter 2: Skill Format and Loader Architecture](02-skill-format-and-loader-architecture.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- tutorial slug: **openskills-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Openskills Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSkills Repository](https://github.com/numman-ali/openskills)
+- [OpenSkills Releases](https://github.com/numman-ali/openskills/releases)
+- [OpenSkills npm package](https://www.npmjs.com/package/openskills)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [HumanLayer Tutorial](../humanlayer-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 1: Getting Started
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `openskills`, `install`, `anthropics` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `skills`, `sync` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `openskills`.
+2. **Input normalization**: shape incoming data so `install` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `anthropics`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSkills Repository](https://github.com/numman-ali/openskills)
+  Why it matters: authoritative reference on `OpenSkills Repository` (github.com).
+- [OpenSkills Releases](https://github.com/numman-ali/openskills/releases)
+  Why it matters: authoritative reference on `OpenSkills Releases` (github.com).
+- [OpenSkills npm package](https://www.npmjs.com/package/openskills)
+  Why it matters: authoritative reference on `OpenSkills npm package` (www.npmjs.com).
+
+Suggested trace strategy:
+- search upstream code for `openskills` and `install` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Skill Format and Loader Architecture](02-skill-format-and-loader-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openskills-tutorial/02-skill-format-and-loader-architecture.md b/tutorials/openskills-tutorial/02-skill-format-and-loader-architecture.md
index 64c725b2..b5b58bd3 100644
--- a/tutorials/openskills-tutorial/02-skill-format-and-loader-architecture.md
+++ b/tutorials/openskills-tutorial/02-skill-format-and-loader-architecture.md
@@ -7,6 +7,9 @@ parent: OpenSkills Tutorial
 
 # Chapter 2: Skill Format and Loader Architecture
 
+Welcome to **Chapter 2: Skill Format and Loader Architecture**. In this part of **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 OpenSkills uses Claude-style `SKILL.md` and generates an agent-readable skills registry in `AGENTS.md`.
 
 ## Architecture Highlights
@@ -22,3 +25,616 @@ OpenSkills uses Claude-style `SKILL.md` and generates an agent-readable skills r
 You now understand how OpenSkills maps skill files into runtime-usable metadata.
 
 Next: [Chapter 3: Installation Sources and Trust Model](03-installation-sources-and-trust-model.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- tutorial slug: **openskills-tutorial**
+- chapter focus: **Chapter 2: Skill Format and Loader Architecture**
+- system context: **Openskills Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Skill Format and Loader Architecture`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSkills Repository](https://github.com/numman-ali/openskills)
+- [OpenSkills Releases](https://github.com/numman-ali/openskills/releases)
+- [OpenSkills npm package](https://www.npmjs.com/package/openskills)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [HumanLayer Tutorial](../humanlayer-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Skill Format and Loader Architecture`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 2: Skill Format and Loader Architecture
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Skill Format and Loader Architecture` as an operating subsystem inside **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Skill Format and Loader Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSkills Repository](https://github.com/numman-ali/openskills)
+  Why it matters: authoritative reference on `OpenSkills Repository` (github.com).
+- [OpenSkills Releases](https://github.com/numman-ali/openskills/releases)
+  Why it matters: authoritative reference on `OpenSkills Releases` (github.com).
+- [OpenSkills npm package](https://www.npmjs.com/package/openskills)
+  Why it matters: authoritative reference on `OpenSkills npm package` (www.npmjs.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Installation Sources and Trust Model](03-installation-sources-and-trust-model.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openskills-tutorial/03-installation-sources-and-trust-model.md b/tutorials/openskills-tutorial/03-installation-sources-and-trust-model.md
index 1ee83708..8601dcbb 100644
--- a/tutorials/openskills-tutorial/03-installation-sources-and-trust-model.md
+++ b/tutorials/openskills-tutorial/03-installation-sources-and-trust-model.md
@@ -7,6 +7,9 @@ parent: OpenSkills Tutorial
 
 # Chapter 3: Installation Sources and Trust Model
 
+Welcome to **Chapter 3: Installation Sources and Trust Model**. In this part of **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 OpenSkills can install from public repos, private repos, and local paths. Trust boundaries should be explicit.
 
 ## Source Types
@@ -22,3 +25,616 @@ OpenSkills can install from public repos, private repos, and local paths. Trust
 You now have a trust model for safe skill installation.
 
 Next: [Chapter 4: Sync and AGENTS.md Integration](04-sync-and-agents-md-integration.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- tutorial slug: **openskills-tutorial**
+- chapter focus: **Chapter 3: Installation Sources and Trust Model**
+- system context: **Openskills Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Installation Sources and Trust Model`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSkills Repository](https://github.com/numman-ali/openskills)
+- [OpenSkills Releases](https://github.com/numman-ali/openskills/releases)
+- [OpenSkills npm package](https://www.npmjs.com/package/openskills)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [HumanLayer Tutorial](../humanlayer-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Installation Sources and Trust Model`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 3: Installation Sources and Trust Model
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Installation Sources and Trust Model` as an operating subsystem inside **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Installation Sources and Trust Model` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSkills Repository](https://github.com/numman-ali/openskills)
+  Why it matters: authoritative reference on `OpenSkills Repository` (github.com).
+- [OpenSkills Releases](https://github.com/numman-ali/openskills/releases)
+  Why it matters: authoritative reference on `OpenSkills Releases` (github.com).
+- [OpenSkills npm package](https://www.npmjs.com/package/openskills)
+  Why it matters: authoritative reference on `OpenSkills npm package` (www.npmjs.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Skill Format and Loader Architecture](02-skill-format-and-loader-architecture.md)
+- [Next Chapter: Chapter 4: Sync and AGENTS.md Integration](04-sync-and-agents-md-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openskills-tutorial/04-sync-and-agents-md-integration.md b/tutorials/openskills-tutorial/04-sync-and-agents-md-integration.md
index 77916221..20a8ef8a 100644
--- a/tutorials/openskills-tutorial/04-sync-and-agents-md-integration.md
+++ b/tutorials/openskills-tutorial/04-sync-and-agents-md-integration.md
@@ -7,6 +7,9 @@ parent: OpenSkills Tutorial
 
 # Chapter 4: Sync and AGENTS.md Integration
 
+Welcome to **Chapter 4: Sync and AGENTS.md Integration**. In this part of **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 `openskills sync` keeps `AGENTS.md` aligned with installed skills so agent clients can discover them.
 
 ## Integration Pattern
@@ -21,3 +24,616 @@ parent: OpenSkills Tutorial
 You now know how to keep skill metadata synchronized and discoverable.
 
 Next: [Chapter 5: Universal Mode and Multi-Agent Setups](05-universal-mode-and-multi-agent-setups.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- tutorial slug: **openskills-tutorial**
+- chapter focus: **Chapter 4: Sync and AGENTS.md Integration**
+- system context: **Openskills Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Sync and AGENTS.md Integration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSkills Repository](https://github.com/numman-ali/openskills)
+- [OpenSkills Releases](https://github.com/numman-ali/openskills/releases)
+- [OpenSkills npm package](https://www.npmjs.com/package/openskills)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [HumanLayer Tutorial](../humanlayer-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Sync and AGENTS.md Integration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 4: Sync and AGENTS.md Integration
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Sync and AGENTS.md Integration` as an operating subsystem inside **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Sync and AGENTS.md Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSkills Repository](https://github.com/numman-ali/openskills)
+  Why it matters: authoritative reference on `OpenSkills Repository` (github.com).
+- [OpenSkills Releases](https://github.com/numman-ali/openskills/releases)
+  Why it matters: authoritative reference on `OpenSkills Releases` (github.com).
+- [OpenSkills npm package](https://www.npmjs.com/package/openskills)
+  Why it matters: authoritative reference on `OpenSkills npm package` (www.npmjs.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Installation Sources and Trust Model](03-installation-sources-and-trust-model.md)
+- [Next Chapter: Chapter 5: Universal Mode and Multi-Agent Setups](05-universal-mode-and-multi-agent-setups.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openskills-tutorial/05-universal-mode-and-multi-agent-setups.md b/tutorials/openskills-tutorial/05-universal-mode-and-multi-agent-setups.md
index 928ab367..72059780 100644
--- a/tutorials/openskills-tutorial/05-universal-mode-and-multi-agent-setups.md
+++ b/tutorials/openskills-tutorial/05-universal-mode-and-multi-agent-setups.md
@@ -7,6 +7,9 @@ parent: OpenSkills Tutorial
 
 # Chapter 5: Universal Mode and Multi-Agent Setups
 
+Welcome to **Chapter 5: Universal Mode and Multi-Agent Setups**. In this part of **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Universal mode helps avoid folder conflicts when multiple agent tools coexist.
 
 ## Priority Order
@@ -21,3 +24,616 @@ Universal mode helps avoid folder conflicts when multiple agent tools coexist.
 You now understand multi-agent layout strategy for stable cross-tool skill usage.
 
 Next: [Chapter 6: Skill Authoring and Packaging](06-skill-authoring-and-packaging.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- tutorial slug: **openskills-tutorial**
+- chapter focus: **Chapter 5: Universal Mode and Multi-Agent Setups**
+- system context: **Openskills Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Universal Mode and Multi-Agent Setups`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSkills Repository](https://github.com/numman-ali/openskills)
+- [OpenSkills Releases](https://github.com/numman-ali/openskills/releases)
+- [OpenSkills npm package](https://www.npmjs.com/package/openskills)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [HumanLayer Tutorial](../humanlayer-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Universal Mode and Multi-Agent Setups`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 5: Universal Mode and Multi-Agent Setups
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Universal Mode and Multi-Agent Setups` as an operating subsystem inside **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Universal Mode and Multi-Agent Setups` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSkills Repository](https://github.com/numman-ali/openskills)
+  Why it matters: authoritative reference on `OpenSkills Repository` (github.com).
+- [OpenSkills Releases](https://github.com/numman-ali/openskills/releases)
+  Why it matters: authoritative reference on `OpenSkills Releases` (github.com).
+- [OpenSkills npm package](https://www.npmjs.com/package/openskills)
+  Why it matters: authoritative reference on `OpenSkills npm package` (www.npmjs.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Sync and AGENTS.md Integration](04-sync-and-agents-md-integration.md)
+- [Next Chapter: Chapter 6: Skill Authoring and Packaging](06-skill-authoring-and-packaging.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openskills-tutorial/06-skill-authoring-and-packaging.md b/tutorials/openskills-tutorial/06-skill-authoring-and-packaging.md
index 83afaeab..06e2d817 100644
--- a/tutorials/openskills-tutorial/06-skill-authoring-and-packaging.md
+++ b/tutorials/openskills-tutorial/06-skill-authoring-and-packaging.md
@@ -7,6 +7,9 @@ parent: OpenSkills Tutorial
 
 # Chapter 6: Skill Authoring and Packaging
 
+Welcome to **Chapter 6: Skill Authoring and Packaging**. In this part of **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Great skills are concise, composable, and resource-backed.
 
 ## Authoring Checklist
@@ -21,3 +24,616 @@ Great skills are concise, composable, and resource-backed.
 You now have a quality baseline for authoring reusable skills.
 
 Next: [Chapter 7: Updates, Versioning, and Governance](07-updates-versioning-and-governance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- tutorial slug: **openskills-tutorial**
+- chapter focus: **Chapter 6: Skill Authoring and Packaging**
+- system context: **Openskills Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Skill Authoring and Packaging`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSkills Repository](https://github.com/numman-ali/openskills)
+- [OpenSkills Releases](https://github.com/numman-ali/openskills/releases)
+- [OpenSkills npm package](https://www.npmjs.com/package/openskills)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [HumanLayer Tutorial](../humanlayer-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Skill Authoring and Packaging`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 6: Skill Authoring and Packaging
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Skill Authoring and Packaging` as an operating subsystem inside **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Skill Authoring and Packaging` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSkills Repository](https://github.com/numman-ali/openskills)
+  Why it matters: authoritative reference on `OpenSkills Repository` (github.com).
+- [OpenSkills Releases](https://github.com/numman-ali/openskills/releases)
+  Why it matters: authoritative reference on `OpenSkills Releases` (github.com).
+- [OpenSkills npm package](https://www.npmjs.com/package/openskills)
+  Why it matters: authoritative reference on `OpenSkills npm package` (www.npmjs.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Universal Mode and Multi-Agent Setups](05-universal-mode-and-multi-agent-setups.md)
+- [Next Chapter: Chapter 7: Updates, Versioning, and Governance](07-updates-versioning-and-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openskills-tutorial/07-updates-versioning-and-governance.md b/tutorials/openskills-tutorial/07-updates-versioning-and-governance.md
index 696299ca..b62868d3 100644
--- a/tutorials/openskills-tutorial/07-updates-versioning-and-governance.md
+++ b/tutorials/openskills-tutorial/07-updates-versioning-and-governance.md
@@ -7,6 +7,9 @@ parent: OpenSkills Tutorial
 
 # Chapter 7: Updates, Versioning, and Governance
 
+Welcome to **Chapter 7: Updates, Versioning, and Governance**. In this part of **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Skill libraries need explicit update and governance policy to avoid drift.
 
 ## Governance Pattern
@@ -22,3 +25,616 @@ Skill libraries need explicit update and governance policy to avoid drift.
 You now have a lifecycle process for maintaining shared skill repositories.
 
 Next: [Chapter 8: Production Security and Operations](08-production-security-and-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- tutorial slug: **openskills-tutorial**
+- chapter focus: **Chapter 7: Updates, Versioning, and Governance**
+- system context: **Openskills Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Updates, Versioning, and Governance`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSkills Repository](https://github.com/numman-ali/openskills)
+- [OpenSkills Releases](https://github.com/numman-ali/openskills/releases)
+- [OpenSkills npm package](https://www.npmjs.com/package/openskills)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [HumanLayer Tutorial](../humanlayer-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Updates, Versioning, and Governance`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 7: Updates, Versioning, and Governance
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Updates, Versioning, and Governance` as an operating subsystem inside **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Updates, Versioning, and Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSkills Repository](https://github.com/numman-ali/openskills)
+  Why it matters: authoritative reference on `OpenSkills Repository` (github.com).
+- [OpenSkills Releases](https://github.com/numman-ali/openskills/releases)
+  Why it matters: authoritative reference on `OpenSkills Releases` (github.com).
+- [OpenSkills npm package](https://www.npmjs.com/package/openskills)
+  Why it matters: authoritative reference on `OpenSkills npm package` (www.npmjs.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Skill Authoring and Packaging](06-skill-authoring-and-packaging.md)
+- [Next Chapter: Chapter 8: Production Security and Operations](08-production-security-and-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openskills-tutorial/08-production-security-and-operations.md b/tutorials/openskills-tutorial/08-production-security-and-operations.md
index 5f384300..b5d84467 100644
--- a/tutorials/openskills-tutorial/08-production-security-and-operations.md
+++ b/tutorials/openskills-tutorial/08-production-security-and-operations.md
@@ -7,6 +7,9 @@ parent: OpenSkills Tutorial
 
 # Chapter 8: Production Security and Operations
 
+Welcome to **Chapter 8: Production Security and Operations**. In this part of **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter defines the baseline for operating OpenSkills at team scale.
 
 ## Security Controls
@@ -19,3 +22,615 @@ This chapter defines the baseline for operating OpenSkills at team scale.
 ## Summary
 
 You now have an operations baseline for enterprise-grade skill distribution.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- tutorial slug: **openskills-tutorial**
+- chapter focus: **Chapter 8: Production Security and Operations**
+- system context: **Openskills Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Security and Operations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSkills Repository](https://github.com/numman-ali/openskills)
+- [OpenSkills Releases](https://github.com/numman-ali/openskills/releases)
+- [OpenSkills npm package](https://www.npmjs.com/package/openskills)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [HumanLayer Tutorial](../humanlayer-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Security and Operations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 8: Production Security and Operations
+
+- tutorial context: **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Security and Operations` as an operating subsystem inside **OpenSkills Tutorial: Universal Skill Loading for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Security and Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSkills Repository](https://github.com/numman-ali/openskills)
+  Why it matters: authoritative reference on `OpenSkills Repository` (github.com).
+- [OpenSkills Releases](https://github.com/numman-ali/openskills/releases)
+  Why it matters: authoritative reference on `OpenSkills Releases` (github.com).
+- [OpenSkills npm package](https://www.npmjs.com/package/openskills)
+  Why it matters: authoritative reference on `OpenSkills npm package` (www.npmjs.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Updates, Versioning, and Governance](07-updates-versioning-and-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openspec-tutorial/01-getting-started-and-opsx-basics.md b/tutorials/openspec-tutorial/01-getting-started-and-opsx-basics.md
index d8bfb98e..23a7a2dc 100644
--- a/tutorials/openspec-tutorial/01-getting-started-and-opsx-basics.md
+++ b/tutorials/openspec-tutorial/01-getting-started-and-opsx-basics.md
@@ -7,6 +7,9 @@ parent: OpenSpec Tutorial
 
 # Chapter 1: Getting Started and OPSX Basics
 
+Welcome to **Chapter 1: Getting Started and OPSX Basics**. In this part of **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter establishes a reliable OpenSpec baseline and clarifies the core OPSX command model.
 
 ## Learning Goals
@@ -59,3 +62,586 @@ openspec init
 You now have a working OpenSpec environment with the core workflow entry points.
 
 Next: [Chapter 2: Artifact Graph and Change Lifecycle](02-artifact-graph-and-change-lifecycle.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- tutorial slug: **openspec-tutorial**
+- chapter focus: **Chapter 1: Getting Started and OPSX Basics**
+- system context: **Openspec Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started and OPSX Basics`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSpec Repository](https://github.com/Fission-AI/OpenSpec)
+- [README](https://github.com/Fission-AI/OpenSpec/blob/main/README.md)
+- [Getting Started](https://github.com/Fission-AI/OpenSpec/blob/main/docs/getting-started.md)
+- [Concepts](https://github.com/Fission-AI/OpenSpec/blob/main/docs/concepts.md)
+- [Workflows](https://github.com/Fission-AI/OpenSpec/blob/main/docs/workflows.md)
+- [Commands](https://github.com/Fission-AI/OpenSpec/blob/main/docs/commands.md)
+- [CLI Reference](https://github.com/Fission-AI/OpenSpec/blob/main/docs/cli.md)
+- [Customization](https://github.com/Fission-AI/OpenSpec/blob/main/docs/customization.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Task Master Tutorial](../claude-task-master-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Chapter 1: Getting Started and OPSX Basics](01-getting-started-and-opsx-basics.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started and OPSX Basics`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started and OPSX Basics
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `openspec`, `install`, `fission` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started and OPSX Basics` as an operating subsystem inside **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `latest`, `your`, `project` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started and OPSX Basics` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `openspec`.
+2. **Input normalization**: shape incoming data so `install` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `fission`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSpec Repository](https://github.com/Fission-AI/OpenSpec)
+  Why it matters: authoritative reference on `OpenSpec Repository` (github.com).
+- [README](https://github.com/Fission-AI/OpenSpec/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Getting Started](https://github.com/Fission-AI/OpenSpec/blob/main/docs/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Concepts](https://github.com/Fission-AI/OpenSpec/blob/main/docs/concepts.md)
+  Why it matters: authoritative reference on `Concepts` (github.com).
+- [Workflows](https://github.com/Fission-AI/OpenSpec/blob/main/docs/workflows.md)
+  Why it matters: authoritative reference on `Workflows` (github.com).
+- [Commands](https://github.com/Fission-AI/OpenSpec/blob/main/docs/commands.md)
+  Why it matters: authoritative reference on `Commands` (github.com).
+- [CLI Reference](https://github.com/Fission-AI/OpenSpec/blob/main/docs/cli.md)
+  Why it matters: authoritative reference on `CLI Reference` (github.com).
+- [Customization](https://github.com/Fission-AI/OpenSpec/blob/main/docs/customization.md)
+  Why it matters: authoritative reference on `Customization` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `openspec` and `install` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Artifact Graph and Change Lifecycle](02-artifact-graph-and-change-lifecycle.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openspec-tutorial/02-artifact-graph-and-change-lifecycle.md b/tutorials/openspec-tutorial/02-artifact-graph-and-change-lifecycle.md
index f17286e9..aef8bd62 100644
--- a/tutorials/openspec-tutorial/02-artifact-graph-and-change-lifecycle.md
+++ b/tutorials/openspec-tutorial/02-artifact-graph-and-change-lifecycle.md
@@ -7,6 +7,9 @@ parent: OpenSpec Tutorial
 
 # Chapter 2: Artifact Graph and Change Lifecycle
 
+Welcome to **Chapter 2: Artifact Graph and Change Lifecycle**. In this part of **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 OpenSpec is strongest when teams treat artifacts as a connected lifecycle, not isolated markdown files.
 
 ## Learning Goals
@@ -55,3 +58,587 @@ flowchart LR
 You now have a working model for how artifacts evolve from intent to archived behavior changes.
 
 Next: [Chapter 3: Command Surface and Agent Workflows](03-command-surface-and-agent-workflows.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- tutorial slug: **openspec-tutorial**
+- chapter focus: **Chapter 2: Artifact Graph and Change Lifecycle**
+- system context: **Openspec Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Artifact Graph and Change Lifecycle`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSpec Repository](https://github.com/Fission-AI/OpenSpec)
+- [README](https://github.com/Fission-AI/OpenSpec/blob/main/README.md)
+- [Getting Started](https://github.com/Fission-AI/OpenSpec/blob/main/docs/getting-started.md)
+- [Concepts](https://github.com/Fission-AI/OpenSpec/blob/main/docs/concepts.md)
+- [Workflows](https://github.com/Fission-AI/OpenSpec/blob/main/docs/workflows.md)
+- [Commands](https://github.com/Fission-AI/OpenSpec/blob/main/docs/commands.md)
+- [CLI Reference](https://github.com/Fission-AI/OpenSpec/blob/main/docs/cli.md)
+- [Customization](https://github.com/Fission-AI/OpenSpec/blob/main/docs/customization.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Task Master Tutorial](../claude-task-master-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Chapter 1: Getting Started and OPSX Basics](01-getting-started-and-opsx-basics.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Artifact Graph and Change Lifecycle`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Artifact Graph and Change Lifecycle
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `specs`, `proposal`, `delta` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Artifact Graph and Change Lifecycle` as an operating subsystem inside **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `design`, `tasks`, `openspec` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Artifact Graph and Change Lifecycle` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `specs`.
+2. **Input normalization**: shape incoming data so `proposal` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `delta`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSpec Repository](https://github.com/Fission-AI/OpenSpec)
+  Why it matters: authoritative reference on `OpenSpec Repository` (github.com).
+- [README](https://github.com/Fission-AI/OpenSpec/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Getting Started](https://github.com/Fission-AI/OpenSpec/blob/main/docs/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Concepts](https://github.com/Fission-AI/OpenSpec/blob/main/docs/concepts.md)
+  Why it matters: authoritative reference on `Concepts` (github.com).
+- [Workflows](https://github.com/Fission-AI/OpenSpec/blob/main/docs/workflows.md)
+  Why it matters: authoritative reference on `Workflows` (github.com).
+- [Commands](https://github.com/Fission-AI/OpenSpec/blob/main/docs/commands.md)
+  Why it matters: authoritative reference on `Commands` (github.com).
+- [CLI Reference](https://github.com/Fission-AI/OpenSpec/blob/main/docs/cli.md)
+  Why it matters: authoritative reference on `CLI Reference` (github.com).
+- [Customization](https://github.com/Fission-AI/OpenSpec/blob/main/docs/customization.md)
+  Why it matters: authoritative reference on `Customization` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `specs` and `proposal` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started and OPSX Basics](01-getting-started-and-opsx-basics.md)
+- [Next Chapter: Chapter 3: Command Surface and Agent Workflows](03-command-surface-and-agent-workflows.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openspec-tutorial/03-command-surface-and-agent-workflows.md b/tutorials/openspec-tutorial/03-command-surface-and-agent-workflows.md
index a407bb57..08e433aa 100644
--- a/tutorials/openspec-tutorial/03-command-surface-and-agent-workflows.md
+++ b/tutorials/openspec-tutorial/03-command-surface-and-agent-workflows.md
@@ -7,6 +7,9 @@ parent: OpenSpec Tutorial
 
 # Chapter 3: Command Surface and Agent Workflows
 
+Welcome to **Chapter 3: Command Surface and Agent Workflows**. In this part of **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter separates human CLI operations from agent-facing commands so workflows stay predictable.
 
 ## Learning Goals
@@ -50,3 +53,599 @@ openspec list --json
 You now know how to coordinate human and agent command usage without workflow collisions.
 
 Next: [Chapter 4: Spec Authoring, Delta Patterns, and Quality](04-spec-authoring-delta-patterns-and-quality.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- tutorial slug: **openspec-tutorial**
+- chapter focus: **Chapter 3: Command Surface and Agent Workflows**
+- system context: **Openspec Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Command Surface and Agent Workflows`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSpec Repository](https://github.com/Fission-AI/OpenSpec)
+- [README](https://github.com/Fission-AI/OpenSpec/blob/main/README.md)
+- [Getting Started](https://github.com/Fission-AI/OpenSpec/blob/main/docs/getting-started.md)
+- [Concepts](https://github.com/Fission-AI/OpenSpec/blob/main/docs/concepts.md)
+- [Workflows](https://github.com/Fission-AI/OpenSpec/blob/main/docs/workflows.md)
+- [Commands](https://github.com/Fission-AI/OpenSpec/blob/main/docs/commands.md)
+- [CLI Reference](https://github.com/Fission-AI/OpenSpec/blob/main/docs/cli.md)
+- [Customization](https://github.com/Fission-AI/OpenSpec/blob/main/docs/customization.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Task Master Tutorial](../claude-task-master-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Chapter 1: Getting Started and OPSX Basics](01-getting-started-and-opsx-basics.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Command Surface and Agent Workflows`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Command Surface and Agent Workflows
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `openspec`, `json`, `status` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Command Surface and Agent Workflows` as an operating subsystem inside **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `validate`, `list` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Command Surface and Agent Workflows` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `openspec`.
+2. **Input normalization**: shape incoming data so `json` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `status`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSpec Repository](https://github.com/Fission-AI/OpenSpec)
+  Why it matters: authoritative reference on `OpenSpec Repository` (github.com).
+- [README](https://github.com/Fission-AI/OpenSpec/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Getting Started](https://github.com/Fission-AI/OpenSpec/blob/main/docs/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Concepts](https://github.com/Fission-AI/OpenSpec/blob/main/docs/concepts.md)
+  Why it matters: authoritative reference on `Concepts` (github.com).
+- [Workflows](https://github.com/Fission-AI/OpenSpec/blob/main/docs/workflows.md)
+  Why it matters: authoritative reference on `Workflows` (github.com).
+- [Commands](https://github.com/Fission-AI/OpenSpec/blob/main/docs/commands.md)
+  Why it matters: authoritative reference on `Commands` (github.com).
+- [CLI Reference](https://github.com/Fission-AI/OpenSpec/blob/main/docs/cli.md)
+  Why it matters: authoritative reference on `CLI Reference` (github.com).
+- [Customization](https://github.com/Fission-AI/OpenSpec/blob/main/docs/customization.md)
+  Why it matters: authoritative reference on `Customization` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `openspec` and `json` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Artifact Graph and Change Lifecycle](02-artifact-graph-and-change-lifecycle.md)
+- [Next Chapter: Chapter 4: Spec Authoring, Delta Patterns, and Quality](04-spec-authoring-delta-patterns-and-quality.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openspec-tutorial/04-spec-authoring-delta-patterns-and-quality.md b/tutorials/openspec-tutorial/04-spec-authoring-delta-patterns-and-quality.md
index cc16442d..062c8659 100644
--- a/tutorials/openspec-tutorial/04-spec-authoring-delta-patterns-and-quality.md
+++ b/tutorials/openspec-tutorial/04-spec-authoring-delta-patterns-and-quality.md
@@ -7,6 +7,9 @@ parent: OpenSpec Tutorial
 
 # Chapter 4: Spec Authoring, Delta Patterns, and Quality
 
+Welcome to **Chapter 4: Spec Authoring, Delta Patterns, and Quality**. In this part of **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Delta spec quality determines whether OpenSpec increases predictability or just adds paperwork.
 
 ## Learning Goals
@@ -54,3 +57,587 @@ Delta spec quality determines whether OpenSpec increases predictability or just
 You now have concrete rules for writing high-signal artifacts that agents and humans can execute against.
 
 Next: [Chapter 5: Customization, Schemas, and Project Rules](05-customization-schemas-and-project-rules.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- tutorial slug: **openspec-tutorial**
+- chapter focus: **Chapter 4: Spec Authoring, Delta Patterns, and Quality**
+- system context: **Openspec Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Spec Authoring, Delta Patterns, and Quality`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSpec Repository](https://github.com/Fission-AI/OpenSpec)
+- [README](https://github.com/Fission-AI/OpenSpec/blob/main/README.md)
+- [Getting Started](https://github.com/Fission-AI/OpenSpec/blob/main/docs/getting-started.md)
+- [Concepts](https://github.com/Fission-AI/OpenSpec/blob/main/docs/concepts.md)
+- [Workflows](https://github.com/Fission-AI/OpenSpec/blob/main/docs/workflows.md)
+- [Commands](https://github.com/Fission-AI/OpenSpec/blob/main/docs/commands.md)
+- [CLI Reference](https://github.com/Fission-AI/OpenSpec/blob/main/docs/cli.md)
+- [Customization](https://github.com/Fission-AI/OpenSpec/blob/main/docs/customization.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Task Master Tutorial](../claude-task-master-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Chapter 1: Getting Started and OPSX Basics](01-getting-started-and-opsx-basics.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Spec Authoring, Delta Patterns, and Quality`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Spec Authoring, Delta Patterns, and Quality
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Requirements`, `Requirement`, `Behavior` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Spec Authoring, Delta Patterns, and Quality` as an operating subsystem inside **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `ADDED`, `Feature`, `MODIFIED` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Spec Authoring, Delta Patterns, and Quality` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Requirements`.
+2. **Input normalization**: shape incoming data so `Requirement` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Behavior`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSpec Repository](https://github.com/Fission-AI/OpenSpec)
+  Why it matters: authoritative reference on `OpenSpec Repository` (github.com).
+- [README](https://github.com/Fission-AI/OpenSpec/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Getting Started](https://github.com/Fission-AI/OpenSpec/blob/main/docs/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Concepts](https://github.com/Fission-AI/OpenSpec/blob/main/docs/concepts.md)
+  Why it matters: authoritative reference on `Concepts` (github.com).
+- [Workflows](https://github.com/Fission-AI/OpenSpec/blob/main/docs/workflows.md)
+  Why it matters: authoritative reference on `Workflows` (github.com).
+- [Commands](https://github.com/Fission-AI/OpenSpec/blob/main/docs/commands.md)
+  Why it matters: authoritative reference on `Commands` (github.com).
+- [CLI Reference](https://github.com/Fission-AI/OpenSpec/blob/main/docs/cli.md)
+  Why it matters: authoritative reference on `CLI Reference` (github.com).
+- [Customization](https://github.com/Fission-AI/OpenSpec/blob/main/docs/customization.md)
+  Why it matters: authoritative reference on `Customization` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Requirements` and `Requirement` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Command Surface and Agent Workflows](03-command-surface-and-agent-workflows.md)
+- [Next Chapter: Chapter 5: Customization, Schemas, and Project Rules](05-customization-schemas-and-project-rules.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openspec-tutorial/05-customization-schemas-and-project-rules.md b/tutorials/openspec-tutorial/05-customization-schemas-and-project-rules.md
index 728f1436..1c4045d1 100644
--- a/tutorials/openspec-tutorial/05-customization-schemas-and-project-rules.md
+++ b/tutorials/openspec-tutorial/05-customization-schemas-and-project-rules.md
@@ -7,6 +7,9 @@ parent: OpenSpec Tutorial
 
 # Chapter 5: Customization, Schemas, and Project Rules
 
+Welcome to **Chapter 5: Customization, Schemas, and Project Rules**. In this part of **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 OpenSpec can be tailored to your engineering environment through configuration and schema controls.
 
 ## Learning Goals
@@ -57,3 +60,587 @@ rules:
 You now know how to shape OpenSpec behavior while keeping workflows maintainable across teams.
 
 Next: [Chapter 6: Tool Integrations and Multi-Agent Portability](06-tool-integrations-and-multi-agent-portability.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- tutorial slug: **openspec-tutorial**
+- chapter focus: **Chapter 5: Customization, Schemas, and Project Rules**
+- system context: **Openspec Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Customization, Schemas, and Project Rules`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSpec Repository](https://github.com/Fission-AI/OpenSpec)
+- [README](https://github.com/Fission-AI/OpenSpec/blob/main/README.md)
+- [Getting Started](https://github.com/Fission-AI/OpenSpec/blob/main/docs/getting-started.md)
+- [Concepts](https://github.com/Fission-AI/OpenSpec/blob/main/docs/concepts.md)
+- [Workflows](https://github.com/Fission-AI/OpenSpec/blob/main/docs/workflows.md)
+- [Commands](https://github.com/Fission-AI/OpenSpec/blob/main/docs/commands.md)
+- [CLI Reference](https://github.com/Fission-AI/OpenSpec/blob/main/docs/cli.md)
+- [Customization](https://github.com/Fission-AI/OpenSpec/blob/main/docs/customization.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Task Master Tutorial](../claude-task-master-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Chapter 1: Getting Started and OPSX Basics](01-getting-started-and-opsx-basics.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Customization, Schemas, and Project Rules`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Customization, Schemas, and Project Rules
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `schema`, `spec`, `driven` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Customization, Schemas, and Project Rules` as an operating subsystem inside **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `context`, `Tech`, `stack` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Customization, Schemas, and Project Rules` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `schema`.
+2. **Input normalization**: shape incoming data so `spec` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `driven`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSpec Repository](https://github.com/Fission-AI/OpenSpec)
+  Why it matters: authoritative reference on `OpenSpec Repository` (github.com).
+- [README](https://github.com/Fission-AI/OpenSpec/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Getting Started](https://github.com/Fission-AI/OpenSpec/blob/main/docs/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Concepts](https://github.com/Fission-AI/OpenSpec/blob/main/docs/concepts.md)
+  Why it matters: authoritative reference on `Concepts` (github.com).
+- [Workflows](https://github.com/Fission-AI/OpenSpec/blob/main/docs/workflows.md)
+  Why it matters: authoritative reference on `Workflows` (github.com).
+- [Commands](https://github.com/Fission-AI/OpenSpec/blob/main/docs/commands.md)
+  Why it matters: authoritative reference on `Commands` (github.com).
+- [CLI Reference](https://github.com/Fission-AI/OpenSpec/blob/main/docs/cli.md)
+  Why it matters: authoritative reference on `CLI Reference` (github.com).
+- [Customization](https://github.com/Fission-AI/OpenSpec/blob/main/docs/customization.md)
+  Why it matters: authoritative reference on `Customization` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `schema` and `spec` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Spec Authoring, Delta Patterns, and Quality](04-spec-authoring-delta-patterns-and-quality.md)
+- [Next Chapter: Chapter 6: Tool Integrations and Multi-Agent Portability](06-tool-integrations-and-multi-agent-portability.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openspec-tutorial/06-tool-integrations-and-multi-agent-portability.md b/tutorials/openspec-tutorial/06-tool-integrations-and-multi-agent-portability.md
index 2019f694..e1821ae1 100644
--- a/tutorials/openspec-tutorial/06-tool-integrations-and-multi-agent-portability.md
+++ b/tutorials/openspec-tutorial/06-tool-integrations-and-multi-agent-portability.md
@@ -7,6 +7,9 @@ parent: OpenSpec Tutorial
 
 # Chapter 6: Tool Integrations and Multi-Agent Portability
 
+Welcome to **Chapter 6: Tool Integrations and Multi-Agent Portability**. In this part of **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 A major OpenSpec strength is tool portability: one workflow, many coding assistants.
 
 ## Learning Goals
@@ -52,3 +55,595 @@ A major OpenSpec strength is tool portability: one workflow, many coding assista
 You now understand how OpenSpec reduces migration friction across coding-agent clients.
 
 Next: [Chapter 7: Validation, Automation, and CI Operations](07-validation-automation-and-ci-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- tutorial slug: **openspec-tutorial**
+- chapter focus: **Chapter 6: Tool Integrations and Multi-Agent Portability**
+- system context: **Openspec Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Tool Integrations and Multi-Agent Portability`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSpec Repository](https://github.com/Fission-AI/OpenSpec)
+- [README](https://github.com/Fission-AI/OpenSpec/blob/main/README.md)
+- [Getting Started](https://github.com/Fission-AI/OpenSpec/blob/main/docs/getting-started.md)
+- [Concepts](https://github.com/Fission-AI/OpenSpec/blob/main/docs/concepts.md)
+- [Workflows](https://github.com/Fission-AI/OpenSpec/blob/main/docs/workflows.md)
+- [Commands](https://github.com/Fission-AI/OpenSpec/blob/main/docs/commands.md)
+- [CLI Reference](https://github.com/Fission-AI/OpenSpec/blob/main/docs/cli.md)
+- [Customization](https://github.com/Fission-AI/OpenSpec/blob/main/docs/customization.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Task Master Tutorial](../claude-task-master-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Chapter 1: Getting Started and OPSX Basics](01-getting-started-and-opsx-basics.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Tool Integrations and Multi-Agent Portability`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Tool Integrations and Multi-Agent Portability
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Tool Integrations and Multi-Agent Portability` as an operating subsystem inside **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Tool Integrations and Multi-Agent Portability` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSpec Repository](https://github.com/Fission-AI/OpenSpec)
+  Why it matters: authoritative reference on `OpenSpec Repository` (github.com).
+- [README](https://github.com/Fission-AI/OpenSpec/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Getting Started](https://github.com/Fission-AI/OpenSpec/blob/main/docs/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Concepts](https://github.com/Fission-AI/OpenSpec/blob/main/docs/concepts.md)
+  Why it matters: authoritative reference on `Concepts` (github.com).
+- [Workflows](https://github.com/Fission-AI/OpenSpec/blob/main/docs/workflows.md)
+  Why it matters: authoritative reference on `Workflows` (github.com).
+- [Commands](https://github.com/Fission-AI/OpenSpec/blob/main/docs/commands.md)
+  Why it matters: authoritative reference on `Commands` (github.com).
+- [CLI Reference](https://github.com/Fission-AI/OpenSpec/blob/main/docs/cli.md)
+  Why it matters: authoritative reference on `CLI Reference` (github.com).
+- [Customization](https://github.com/Fission-AI/OpenSpec/blob/main/docs/customization.md)
+  Why it matters: authoritative reference on `Customization` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Customization, Schemas, and Project Rules](05-customization-schemas-and-project-rules.md)
+- [Next Chapter: Chapter 7: Validation, Automation, and CI Operations](07-validation-automation-and-ci-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openspec-tutorial/07-validation-automation-and-ci-operations.md b/tutorials/openspec-tutorial/07-validation-automation-and-ci-operations.md
index 84963c3c..e0caa5e6 100644
--- a/tutorials/openspec-tutorial/07-validation-automation-and-ci-operations.md
+++ b/tutorials/openspec-tutorial/07-validation-automation-and-ci-operations.md
@@ -7,6 +7,9 @@ parent: OpenSpec Tutorial
 
 # Chapter 7: Validation, Automation, and CI Operations
 
+Welcome to **Chapter 7: Validation, Automation, and CI Operations**. In this part of **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on quality gates so OpenSpec artifacts remain trusted inputs to implementation.
 
 ## Learning Goals
@@ -55,3 +58,587 @@ openspec status --json
 You now have an actionable quality-gate model for integrating OpenSpec into CI/CD.
 
 Next: [Chapter 8: Migration, Governance, and Team Adoption](08-migration-governance-and-team-adoption.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- tutorial slug: **openspec-tutorial**
+- chapter focus: **Chapter 7: Validation, Automation, and CI Operations**
+- system context: **Openspec Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Validation, Automation, and CI Operations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSpec Repository](https://github.com/Fission-AI/OpenSpec)
+- [README](https://github.com/Fission-AI/OpenSpec/blob/main/README.md)
+- [Getting Started](https://github.com/Fission-AI/OpenSpec/blob/main/docs/getting-started.md)
+- [Concepts](https://github.com/Fission-AI/OpenSpec/blob/main/docs/concepts.md)
+- [Workflows](https://github.com/Fission-AI/OpenSpec/blob/main/docs/workflows.md)
+- [Commands](https://github.com/Fission-AI/OpenSpec/blob/main/docs/commands.md)
+- [CLI Reference](https://github.com/Fission-AI/OpenSpec/blob/main/docs/cli.md)
+- [Customization](https://github.com/Fission-AI/OpenSpec/blob/main/docs/customization.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Task Master Tutorial](../claude-task-master-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Chapter 1: Getting Started and OPSX Basics](01-getting-started-and-opsx-basics.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Validation, Automation, and CI Operations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Validation, Automation, and CI Operations
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `openspec`, `validate`, `status` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Validation, Automation, and CI Operations` as an operating subsystem inside **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `json`, `instructions`, `proposal` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Validation, Automation, and CI Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `openspec`.
+2. **Input normalization**: shape incoming data so `validate` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `status`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSpec Repository](https://github.com/Fission-AI/OpenSpec)
+  Why it matters: authoritative reference on `OpenSpec Repository` (github.com).
+- [README](https://github.com/Fission-AI/OpenSpec/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Getting Started](https://github.com/Fission-AI/OpenSpec/blob/main/docs/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Concepts](https://github.com/Fission-AI/OpenSpec/blob/main/docs/concepts.md)
+  Why it matters: authoritative reference on `Concepts` (github.com).
+- [Workflows](https://github.com/Fission-AI/OpenSpec/blob/main/docs/workflows.md)
+  Why it matters: authoritative reference on `Workflows` (github.com).
+- [Commands](https://github.com/Fission-AI/OpenSpec/blob/main/docs/commands.md)
+  Why it matters: authoritative reference on `Commands` (github.com).
+- [CLI Reference](https://github.com/Fission-AI/OpenSpec/blob/main/docs/cli.md)
+  Why it matters: authoritative reference on `CLI Reference` (github.com).
+- [Customization](https://github.com/Fission-AI/OpenSpec/blob/main/docs/customization.md)
+  Why it matters: authoritative reference on `Customization` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `openspec` and `validate` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Tool Integrations and Multi-Agent Portability](06-tool-integrations-and-multi-agent-portability.md)
+- [Next Chapter: Chapter 8: Migration, Governance, and Team Adoption](08-migration-governance-and-team-adoption.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/openspec-tutorial/08-migration-governance-and-team-adoption.md b/tutorials/openspec-tutorial/08-migration-governance-and-team-adoption.md
index 76d9822a..1647e3f6 100644
--- a/tutorials/openspec-tutorial/08-migration-governance-and-team-adoption.md
+++ b/tutorials/openspec-tutorial/08-migration-governance-and-team-adoption.md
@@ -7,6 +7,9 @@ parent: OpenSpec Tutorial
 
 # Chapter 8: Migration, Governance, and Team Adoption
 
+Welcome to **Chapter 8: Migration, Governance, and Team Adoption**. In this part of **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This final chapter covers migration from legacy workflows and long-term team operating practices.
 
 ## Learning Goals
@@ -49,3 +52,594 @@ This final chapter covers migration from legacy workflows and long-term team ope
 You now have an end-to-end model for running OpenSpec as part of a production engineering workflow.
 
 Next: compare execution patterns with [Claude Task Master](../claude-task-master-tutorial/) and [Codex CLI](../codex-cli-tutorial/).
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- tutorial slug: **openspec-tutorial**
+- chapter focus: **Chapter 8: Migration, Governance, and Team Adoption**
+- system context: **Openspec Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Migration, Governance, and Team Adoption`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSpec Repository](https://github.com/Fission-AI/OpenSpec)
+- [README](https://github.com/Fission-AI/OpenSpec/blob/main/README.md)
+- [Getting Started](https://github.com/Fission-AI/OpenSpec/blob/main/docs/getting-started.md)
+- [Concepts](https://github.com/Fission-AI/OpenSpec/blob/main/docs/concepts.md)
+- [Workflows](https://github.com/Fission-AI/OpenSpec/blob/main/docs/workflows.md)
+- [Commands](https://github.com/Fission-AI/OpenSpec/blob/main/docs/commands.md)
+- [CLI Reference](https://github.com/Fission-AI/OpenSpec/blob/main/docs/cli.md)
+- [Customization](https://github.com/Fission-AI/OpenSpec/blob/main/docs/customization.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Task Master Tutorial](../claude-task-master-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Chapter 1: Getting Started and OPSX Basics](01-getting-started-and-opsx-basics.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Migration, Governance, and Team Adoption`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Migration, Governance, and Team Adoption
+
+- tutorial context: **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Migration, Governance, and Team Adoption` as an operating subsystem inside **OpenSpec Tutorial: Spec-Driven Workflows for AI Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Migration, Governance, and Team Adoption` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSpec Repository](https://github.com/Fission-AI/OpenSpec)
+  Why it matters: authoritative reference on `OpenSpec Repository` (github.com).
+- [README](https://github.com/Fission-AI/OpenSpec/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Getting Started](https://github.com/Fission-AI/OpenSpec/blob/main/docs/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Concepts](https://github.com/Fission-AI/OpenSpec/blob/main/docs/concepts.md)
+  Why it matters: authoritative reference on `Concepts` (github.com).
+- [Workflows](https://github.com/Fission-AI/OpenSpec/blob/main/docs/workflows.md)
+  Why it matters: authoritative reference on `Workflows` (github.com).
+- [Commands](https://github.com/Fission-AI/OpenSpec/blob/main/docs/commands.md)
+  Why it matters: authoritative reference on `Commands` (github.com).
+- [CLI Reference](https://github.com/Fission-AI/OpenSpec/blob/main/docs/cli.md)
+  Why it matters: authoritative reference on `CLI Reference` (github.com).
+- [Customization](https://github.com/Fission-AI/OpenSpec/blob/main/docs/customization.md)
+  Why it matters: authoritative reference on `Customization` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Validation, Automation, and CI Operations](07-validation-automation-and-ci-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/opensrc-tutorial/01-getting-started.md b/tutorials/opensrc-tutorial/01-getting-started.md
index beeb1739..f11798f5 100644
--- a/tutorials/opensrc-tutorial/01-getting-started.md
+++ b/tutorials/opensrc-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: OpenSrc Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **OpenSrc Tutorial: Deep Source Context for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets OpenSrc installed and fetching your first source dependency.
 
 ## Quick Start
@@ -38,3 +41,601 @@ npx opensrc react react-dom
 You now have OpenSrc running with an initial source import and index file.
 
 Next: [Chapter 2: Input Parsing and Resolution Pipeline](02-input-parsing-and-resolution-pipeline.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- tutorial slug: **opensrc-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Opensrc Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSrc Repository](https://github.com/vercel-labs/opensrc)
+- [OpenSrc README](https://github.com/vercel-labs/opensrc/blob/main/README.md)
+- [CLI entrypoint](https://github.com/vercel-labs/opensrc/blob/main/src/index.ts)
+- [Fetch command implementation](https://github.com/vercel-labs/opensrc/blob/main/src/commands/fetch.ts)
+- [Registry resolvers](https://github.com/vercel-labs/opensrc/tree/main/src/lib/registries)
+
+### Cross-Tutorial Connection Map
+
+- [OpenSkills Tutorial](../openskills-tutorial/)
+- [CodeMachine CLI Tutorial](../codemachine-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `opensrc`, `react`, `install` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **OpenSrc Tutorial: Deep Source Context for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `list` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `opensrc`.
+2. **Input normalization**: shape incoming data so `react` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `install`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSrc Repository](https://github.com/vercel-labs/opensrc)
+  Why it matters: authoritative reference on `OpenSrc Repository` (github.com).
+- [OpenSrc README](https://github.com/vercel-labs/opensrc/blob/main/README.md)
+  Why it matters: authoritative reference on `OpenSrc README` (github.com).
+- [CLI entrypoint](https://github.com/vercel-labs/opensrc/blob/main/src/index.ts)
+  Why it matters: authoritative reference on `CLI entrypoint` (github.com).
+- [Fetch command implementation](https://github.com/vercel-labs/opensrc/blob/main/src/commands/fetch.ts)
+  Why it matters: authoritative reference on `Fetch command implementation` (github.com).
+- [Registry resolvers](https://github.com/vercel-labs/opensrc/tree/main/src/lib/registries)
+  Why it matters: authoritative reference on `Registry resolvers` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `opensrc` and `react` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Input Parsing and Resolution Pipeline](02-input-parsing-and-resolution-pipeline.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/opensrc-tutorial/02-input-parsing-and-resolution-pipeline.md b/tutorials/opensrc-tutorial/02-input-parsing-and-resolution-pipeline.md
index 47225336..ba2c03ca 100644
--- a/tutorials/opensrc-tutorial/02-input-parsing-and-resolution-pipeline.md
+++ b/tutorials/opensrc-tutorial/02-input-parsing-and-resolution-pipeline.md
@@ -7,6 +7,9 @@ parent: OpenSrc Tutorial
 
 # Chapter 2: Input Parsing and Resolution Pipeline
 
+Welcome to **Chapter 2: Input Parsing and Resolution Pipeline**. In this part of **OpenSrc Tutorial: Deep Source Context for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 OpenSrc routes each input through parsing logic that determines whether it is a package spec or a direct repository spec.
 
 ## Input Types
@@ -35,3 +38,598 @@ OpenSrc routes each input through parsing logic that determines whether it is a
 You now understand how OpenSrc classifies and routes each input before fetching.
 
 Next: [Chapter 3: Multi-Registry Package Fetching](03-multi-registry-package-fetching.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- tutorial slug: **opensrc-tutorial**
+- chapter focus: **Chapter 2: Input Parsing and Resolution Pipeline**
+- system context: **Opensrc Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Input Parsing and Resolution Pipeline`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSrc Repository](https://github.com/vercel-labs/opensrc)
+- [OpenSrc README](https://github.com/vercel-labs/opensrc/blob/main/README.md)
+- [CLI entrypoint](https://github.com/vercel-labs/opensrc/blob/main/src/index.ts)
+- [Fetch command implementation](https://github.com/vercel-labs/opensrc/blob/main/src/commands/fetch.ts)
+- [Registry resolvers](https://github.com/vercel-labs/opensrc/tree/main/src/lib/registries)
+
+### Cross-Tutorial Connection Map
+
+- [OpenSkills Tutorial](../openskills-tutorial/)
+- [CodeMachine CLI Tutorial](../codemachine-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Input Parsing and Resolution Pipeline`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Input Parsing and Resolution Pipeline
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Input Parsing and Resolution Pipeline` as an operating subsystem inside **OpenSrc Tutorial: Deep Source Context for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Input Parsing and Resolution Pipeline` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSrc Repository](https://github.com/vercel-labs/opensrc)
+  Why it matters: authoritative reference on `OpenSrc Repository` (github.com).
+- [OpenSrc README](https://github.com/vercel-labs/opensrc/blob/main/README.md)
+  Why it matters: authoritative reference on `OpenSrc README` (github.com).
+- [CLI entrypoint](https://github.com/vercel-labs/opensrc/blob/main/src/index.ts)
+  Why it matters: authoritative reference on `CLI entrypoint` (github.com).
+- [Fetch command implementation](https://github.com/vercel-labs/opensrc/blob/main/src/commands/fetch.ts)
+  Why it matters: authoritative reference on `Fetch command implementation` (github.com).
+- [Registry resolvers](https://github.com/vercel-labs/opensrc/tree/main/src/lib/registries)
+  Why it matters: authoritative reference on `Registry resolvers` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Multi-Registry Package Fetching](03-multi-registry-package-fetching.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/opensrc-tutorial/03-multi-registry-package-fetching.md b/tutorials/opensrc-tutorial/03-multi-registry-package-fetching.md
index 6d36dc3a..69cd179c 100644
--- a/tutorials/opensrc-tutorial/03-multi-registry-package-fetching.md
+++ b/tutorials/opensrc-tutorial/03-multi-registry-package-fetching.md
@@ -7,6 +7,9 @@ parent: OpenSrc Tutorial
 
 # Chapter 3: Multi-Registry Package Fetching
 
+Welcome to **Chapter 3: Multi-Registry Package Fetching**. In this part of **OpenSrc Tutorial: Deep Source Context for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 OpenSrc supports package resolution across npm, PyPI, and crates.io using registry-specific metadata paths.
 
 ## Registry Coverage
@@ -42,3 +45,602 @@ opensrc crates:serde
 You now have a model for how OpenSrc maps package ecosystems to repository source retrieval.
 
 Next: [Chapter 4: Git Repository Source Imports](04-git-repository-source-imports.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- tutorial slug: **opensrc-tutorial**
+- chapter focus: **Chapter 3: Multi-Registry Package Fetching**
+- system context: **Opensrc Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Multi-Registry Package Fetching`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSrc Repository](https://github.com/vercel-labs/opensrc)
+- [OpenSrc README](https://github.com/vercel-labs/opensrc/blob/main/README.md)
+- [CLI entrypoint](https://github.com/vercel-labs/opensrc/blob/main/src/index.ts)
+- [Fetch command implementation](https://github.com/vercel-labs/opensrc/blob/main/src/commands/fetch.ts)
+- [Registry resolvers](https://github.com/vercel-labs/opensrc/tree/main/src/lib/registries)
+
+### Cross-Tutorial Connection Map
+
+- [OpenSkills Tutorial](../openskills-tutorial/)
+- [CodeMachine CLI Tutorial](../codemachine-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Multi-Registry Package Fetching`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Multi-Registry Package Fetching
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `opensrc`, `pypi`, `requests` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Multi-Registry Package Fetching` as an operating subsystem inside **OpenSrc Tutorial: Deep Source Context for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `crates`, `serde` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Multi-Registry Package Fetching` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `opensrc`.
+2. **Input normalization**: shape incoming data so `pypi` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `requests`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSrc Repository](https://github.com/vercel-labs/opensrc)
+  Why it matters: authoritative reference on `OpenSrc Repository` (github.com).
+- [OpenSrc README](https://github.com/vercel-labs/opensrc/blob/main/README.md)
+  Why it matters: authoritative reference on `OpenSrc README` (github.com).
+- [CLI entrypoint](https://github.com/vercel-labs/opensrc/blob/main/src/index.ts)
+  Why it matters: authoritative reference on `CLI entrypoint` (github.com).
+- [Fetch command implementation](https://github.com/vercel-labs/opensrc/blob/main/src/commands/fetch.ts)
+  Why it matters: authoritative reference on `Fetch command implementation` (github.com).
+- [Registry resolvers](https://github.com/vercel-labs/opensrc/tree/main/src/lib/registries)
+  Why it matters: authoritative reference on `Registry resolvers` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `opensrc` and `pypi` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Input Parsing and Resolution Pipeline](02-input-parsing-and-resolution-pipeline.md)
+- [Next Chapter: Chapter 4: Git Repository Source Imports](04-git-repository-source-imports.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/opensrc-tutorial/04-git-repository-source-imports.md b/tutorials/opensrc-tutorial/04-git-repository-source-imports.md
index 71540827..3b4475c3 100644
--- a/tutorials/opensrc-tutorial/04-git-repository-source-imports.md
+++ b/tutorials/opensrc-tutorial/04-git-repository-source-imports.md
@@ -7,6 +7,9 @@ parent: OpenSrc Tutorial
 
 # Chapter 4: Git Repository Source Imports
 
+Welcome to **Chapter 4: Git Repository Source Imports**. In this part of **OpenSrc Tutorial: Deep Source Context for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 OpenSrc can fetch direct git repositories when package metadata is not the right entry path.
 
 ## Supported Repo Inputs
@@ -39,3 +42,602 @@ opensrc/
 You now understand how OpenSrc imports repository source directly and normalizes storage paths.
 
 Next: [Chapter 5: AGENTS.md and sources.json Integration](05-agents-md-and-sources-json-integration.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- tutorial slug: **opensrc-tutorial**
+- chapter focus: **Chapter 4: Git Repository Source Imports**
+- system context: **Opensrc Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Git Repository Source Imports`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSrc Repository](https://github.com/vercel-labs/opensrc)
+- [OpenSrc README](https://github.com/vercel-labs/opensrc/blob/main/README.md)
+- [CLI entrypoint](https://github.com/vercel-labs/opensrc/blob/main/src/index.ts)
+- [Fetch command implementation](https://github.com/vercel-labs/opensrc/blob/main/src/commands/fetch.ts)
+- [Registry resolvers](https://github.com/vercel-labs/opensrc/tree/main/src/lib/registries)
+
+### Cross-Tutorial Connection Map
+
+- [OpenSkills Tutorial](../openskills-tutorial/)
+- [CodeMachine CLI Tutorial](../codemachine-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Git Repository Source Imports`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Git Repository Source Imports
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `opensrc`, `repos`, `github` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Git Repository Source Imports` as an operating subsystem inside **OpenSrc Tutorial: Deep Source Context for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `vercel` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Git Repository Source Imports` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `opensrc`.
+2. **Input normalization**: shape incoming data so `repos` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `github`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSrc Repository](https://github.com/vercel-labs/opensrc)
+  Why it matters: authoritative reference on `OpenSrc Repository` (github.com).
+- [OpenSrc README](https://github.com/vercel-labs/opensrc/blob/main/README.md)
+  Why it matters: authoritative reference on `OpenSrc README` (github.com).
+- [CLI entrypoint](https://github.com/vercel-labs/opensrc/blob/main/src/index.ts)
+  Why it matters: authoritative reference on `CLI entrypoint` (github.com).
+- [Fetch command implementation](https://github.com/vercel-labs/opensrc/blob/main/src/commands/fetch.ts)
+  Why it matters: authoritative reference on `Fetch command implementation` (github.com).
+- [Registry resolvers](https://github.com/vercel-labs/opensrc/tree/main/src/lib/registries)
+  Why it matters: authoritative reference on `Registry resolvers` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `opensrc` and `repos` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Multi-Registry Package Fetching](03-multi-registry-package-fetching.md)
+- [Next Chapter: Chapter 5: AGENTS.md and sources.json Integration](05-agents-md-and-sources-json-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/opensrc-tutorial/05-agents-md-and-sources-json-integration.md b/tutorials/opensrc-tutorial/05-agents-md-and-sources-json-integration.md
index 8010e24d..b5dd3214 100644
--- a/tutorials/opensrc-tutorial/05-agents-md-and-sources-json-integration.md
+++ b/tutorials/opensrc-tutorial/05-agents-md-and-sources-json-integration.md
@@ -7,6 +7,9 @@ parent: OpenSrc Tutorial
 
 # Chapter 5: AGENTS.md and sources.json Integration
 
+Welcome to **Chapter 5: AGENTS.md and sources.json Integration**. In this part of **OpenSrc Tutorial: Deep Source Context for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 OpenSrc can update project metadata so coding agents know where imported source context lives.
 
 ## Integration Outputs
@@ -32,3 +35,610 @@ On first run, OpenSrc asks if file modifications are allowed. The preference is
 You now know how OpenSrc surfaces fetched sources to agent workflows without manual file editing.
 
 Next: [Chapter 6: Update, Remove, and Clean Lifecycle](06-update-remove-and-clean-lifecycle.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- tutorial slug: **opensrc-tutorial**
+- chapter focus: **Chapter 5: AGENTS.md and sources.json Integration**
+- system context: **Opensrc Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: AGENTS.md and sources.json Integration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSrc Repository](https://github.com/vercel-labs/opensrc)
+- [OpenSrc README](https://github.com/vercel-labs/opensrc/blob/main/README.md)
+- [CLI entrypoint](https://github.com/vercel-labs/opensrc/blob/main/src/index.ts)
+- [Fetch command implementation](https://github.com/vercel-labs/opensrc/blob/main/src/commands/fetch.ts)
+- [Registry resolvers](https://github.com/vercel-labs/opensrc/tree/main/src/lib/registries)
+
+### Cross-Tutorial Connection Map
+
+- [OpenSkills Tutorial](../openskills-tutorial/)
+- [CodeMachine CLI Tutorial](../codemachine-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: AGENTS.md and sources.json Integration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 5: AGENTS.md and sources.json Integration
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: AGENTS.md and sources.json Integration` as an operating subsystem inside **OpenSrc Tutorial: Deep Source Context for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: AGENTS.md and sources.json Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSrc Repository](https://github.com/vercel-labs/opensrc)
+  Why it matters: authoritative reference on `OpenSrc Repository` (github.com).
+- [OpenSrc README](https://github.com/vercel-labs/opensrc/blob/main/README.md)
+  Why it matters: authoritative reference on `OpenSrc README` (github.com).
+- [CLI entrypoint](https://github.com/vercel-labs/opensrc/blob/main/src/index.ts)
+  Why it matters: authoritative reference on `CLI entrypoint` (github.com).
+- [Fetch command implementation](https://github.com/vercel-labs/opensrc/blob/main/src/commands/fetch.ts)
+  Why it matters: authoritative reference on `Fetch command implementation` (github.com).
+- [Registry resolvers](https://github.com/vercel-labs/opensrc/tree/main/src/lib/registries)
+  Why it matters: authoritative reference on `Registry resolvers` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Git Repository Source Imports](04-git-repository-source-imports.md)
+- [Next Chapter: Chapter 6: Update, Remove, and Clean Lifecycle](06-update-remove-and-clean-lifecycle.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/opensrc-tutorial/06-update-remove-and-clean-lifecycle.md b/tutorials/opensrc-tutorial/06-update-remove-and-clean-lifecycle.md
index ab0d83b1..fe4c7101 100644
--- a/tutorials/opensrc-tutorial/06-update-remove-and-clean-lifecycle.md
+++ b/tutorials/opensrc-tutorial/06-update-remove-and-clean-lifecycle.md
@@ -7,6 +7,9 @@ parent: OpenSrc Tutorial
 
 # Chapter 6: Update, Remove, and Clean Lifecycle
 
+Welcome to **Chapter 6: Update, Remove, and Clean Lifecycle**. In this part of **OpenSrc Tutorial: Deep Source Context for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 OpenSrc includes commands for incremental refresh and cleanup of source caches.
 
 ## Lifecycle Commands
@@ -36,3 +39,602 @@ opensrc clean --npm        # remove only npm package sources
 You now have operational control over source import lifecycle and cache hygiene.
 
 Next: [Chapter 7: Reliability, Rate Limits, and Version Fallbacks](07-reliability-rate-limits-and-version-fallbacks.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- tutorial slug: **opensrc-tutorial**
+- chapter focus: **Chapter 6: Update, Remove, and Clean Lifecycle**
+- system context: **Opensrc Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Update, Remove, and Clean Lifecycle`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSrc Repository](https://github.com/vercel-labs/opensrc)
+- [OpenSrc README](https://github.com/vercel-labs/opensrc/blob/main/README.md)
+- [CLI entrypoint](https://github.com/vercel-labs/opensrc/blob/main/src/index.ts)
+- [Fetch command implementation](https://github.com/vercel-labs/opensrc/blob/main/src/commands/fetch.ts)
+- [Registry resolvers](https://github.com/vercel-labs/opensrc/tree/main/src/lib/registries)
+
+### Cross-Tutorial Connection Map
+
+- [OpenSkills Tutorial](../openskills-tutorial/)
+- [CodeMachine CLI Tutorial](../codemachine-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Update, Remove, and Clean Lifecycle`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Update, Remove, and Clean Lifecycle
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `remove`, `opensrc`, `source` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Update, Remove, and Clean Lifecycle` as an operating subsystem inside **OpenSrc Tutorial: Deep Source Context for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `package`, `clean`, `sources` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Update, Remove, and Clean Lifecycle` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `remove`.
+2. **Input normalization**: shape incoming data so `opensrc` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `source`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSrc Repository](https://github.com/vercel-labs/opensrc)
+  Why it matters: authoritative reference on `OpenSrc Repository` (github.com).
+- [OpenSrc README](https://github.com/vercel-labs/opensrc/blob/main/README.md)
+  Why it matters: authoritative reference on `OpenSrc README` (github.com).
+- [CLI entrypoint](https://github.com/vercel-labs/opensrc/blob/main/src/index.ts)
+  Why it matters: authoritative reference on `CLI entrypoint` (github.com).
+- [Fetch command implementation](https://github.com/vercel-labs/opensrc/blob/main/src/commands/fetch.ts)
+  Why it matters: authoritative reference on `Fetch command implementation` (github.com).
+- [Registry resolvers](https://github.com/vercel-labs/opensrc/tree/main/src/lib/registries)
+  Why it matters: authoritative reference on `Registry resolvers` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `remove` and `opensrc` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: AGENTS.md and sources.json Integration](05-agents-md-and-sources-json-integration.md)
+- [Next Chapter: Chapter 7: Reliability, Rate Limits, and Version Fallbacks](07-reliability-rate-limits-and-version-fallbacks.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/opensrc-tutorial/07-reliability-rate-limits-and-version-fallbacks.md b/tutorials/opensrc-tutorial/07-reliability-rate-limits-and-version-fallbacks.md
index c261e360..7d2d6bb5 100644
--- a/tutorials/opensrc-tutorial/07-reliability-rate-limits-and-version-fallbacks.md
+++ b/tutorials/opensrc-tutorial/07-reliability-rate-limits-and-version-fallbacks.md
@@ -7,6 +7,9 @@ parent: OpenSrc Tutorial
 
 # Chapter 7: Reliability, Rate Limits, and Version Fallbacks
 
+Welcome to **Chapter 7: Reliability, Rate Limits, and Version Fallbacks**. In this part of **OpenSrc Tutorial: Deep Source Context for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Real-world source fetching must account for imperfect metadata, missing tags, and API rate limits.
 
 ## Built-In Fallback Patterns
@@ -31,3 +34,610 @@ Real-world source fetching must account for imperfect metadata, missing tags, an
 You now understand how OpenSrc behaves under common failure modes and how to design safer workflows around them.
 
 Next: [Chapter 8: Team Operations and Governance](08-team-operations-and-governance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- tutorial slug: **opensrc-tutorial**
+- chapter focus: **Chapter 7: Reliability, Rate Limits, and Version Fallbacks**
+- system context: **Opensrc Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Reliability, Rate Limits, and Version Fallbacks`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSrc Repository](https://github.com/vercel-labs/opensrc)
+- [OpenSrc README](https://github.com/vercel-labs/opensrc/blob/main/README.md)
+- [CLI entrypoint](https://github.com/vercel-labs/opensrc/blob/main/src/index.ts)
+- [Fetch command implementation](https://github.com/vercel-labs/opensrc/blob/main/src/commands/fetch.ts)
+- [Registry resolvers](https://github.com/vercel-labs/opensrc/tree/main/src/lib/registries)
+
+### Cross-Tutorial Connection Map
+
+- [OpenSkills Tutorial](../openskills-tutorial/)
+- [CodeMachine CLI Tutorial](../codemachine-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Reliability, Rate Limits, and Version Fallbacks`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 7: Reliability, Rate Limits, and Version Fallbacks
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Reliability, Rate Limits, and Version Fallbacks` as an operating subsystem inside **OpenSrc Tutorial: Deep Source Context for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Reliability, Rate Limits, and Version Fallbacks` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSrc Repository](https://github.com/vercel-labs/opensrc)
+  Why it matters: authoritative reference on `OpenSrc Repository` (github.com).
+- [OpenSrc README](https://github.com/vercel-labs/opensrc/blob/main/README.md)
+  Why it matters: authoritative reference on `OpenSrc README` (github.com).
+- [CLI entrypoint](https://github.com/vercel-labs/opensrc/blob/main/src/index.ts)
+  Why it matters: authoritative reference on `CLI entrypoint` (github.com).
+- [Fetch command implementation](https://github.com/vercel-labs/opensrc/blob/main/src/commands/fetch.ts)
+  Why it matters: authoritative reference on `Fetch command implementation` (github.com).
+- [Registry resolvers](https://github.com/vercel-labs/opensrc/tree/main/src/lib/registries)
+  Why it matters: authoritative reference on `Registry resolvers` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Update, Remove, and Clean Lifecycle](06-update-remove-and-clean-lifecycle.md)
+- [Next Chapter: Chapter 8: Team Operations and Governance](08-team-operations-and-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/opensrc-tutorial/08-team-operations-and-governance.md b/tutorials/opensrc-tutorial/08-team-operations-and-governance.md
index d5b25b2a..0e0f73ff 100644
--- a/tutorials/opensrc-tutorial/08-team-operations-and-governance.md
+++ b/tutorials/opensrc-tutorial/08-team-operations-and-governance.md
@@ -7,6 +7,9 @@ parent: OpenSrc Tutorial
 
 # Chapter 8: Team Operations and Governance
 
+Welcome to **Chapter 8: Team Operations and Governance**. In this part of **OpenSrc Tutorial: Deep Source Context for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 For team usage, OpenSrc works best with explicit policy on what to fetch, where to reference it, and how to keep it current.
 
 ## Team Governance Checklist
@@ -30,3 +33,609 @@ For team usage, OpenSrc works best with explicit policy on what to fetch, where
 ## Summary
 
 You now have a governance baseline for scaling OpenSrc usage across repositories and teams.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- tutorial slug: **opensrc-tutorial**
+- chapter focus: **Chapter 8: Team Operations and Governance**
+- system context: **Opensrc Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Team Operations and Governance`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [OpenSrc Repository](https://github.com/vercel-labs/opensrc)
+- [OpenSrc README](https://github.com/vercel-labs/opensrc/blob/main/README.md)
+- [CLI entrypoint](https://github.com/vercel-labs/opensrc/blob/main/src/index.ts)
+- [Fetch command implementation](https://github.com/vercel-labs/opensrc/blob/main/src/commands/fetch.ts)
+- [Registry resolvers](https://github.com/vercel-labs/opensrc/tree/main/src/lib/registries)
+
+### Cross-Tutorial Connection Map
+
+- [OpenSkills Tutorial](../openskills-tutorial/)
+- [CodeMachine CLI Tutorial](../codemachine-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Team Operations and Governance`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 8: Team Operations and Governance
+
+- tutorial context: **OpenSrc Tutorial: Deep Source Context for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Team Operations and Governance` as an operating subsystem inside **OpenSrc Tutorial: Deep Source Context for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Team Operations and Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [OpenSrc Repository](https://github.com/vercel-labs/opensrc)
+  Why it matters: authoritative reference on `OpenSrc Repository` (github.com).
+- [OpenSrc README](https://github.com/vercel-labs/opensrc/blob/main/README.md)
+  Why it matters: authoritative reference on `OpenSrc README` (github.com).
+- [CLI entrypoint](https://github.com/vercel-labs/opensrc/blob/main/src/index.ts)
+  Why it matters: authoritative reference on `CLI entrypoint` (github.com).
+- [Fetch command implementation](https://github.com/vercel-labs/opensrc/blob/main/src/commands/fetch.ts)
+  Why it matters: authoritative reference on `Fetch command implementation` (github.com).
+- [Registry resolvers](https://github.com/vercel-labs/opensrc/tree/main/src/lib/registries)
+  Why it matters: authoritative reference on `Registry resolvers` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Reliability, Rate Limits, and Version Fallbacks](07-reliability-rate-limits-and-version-fallbacks.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/outlines-tutorial/01-getting-started.md b/tutorials/outlines-tutorial/01-getting-started.md
index d7e48159..a08933ee 100644
--- a/tutorials/outlines-tutorial/01-getting-started.md
+++ b/tutorials/outlines-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: Outlines Tutorial
 
 # Chapter 1: Getting Started with Outlines
 
+Welcome to **Chapter 1: Getting Started with Outlines**. In this part of **Outlines Tutorial: Structured Text Generation with LLMs**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master the fundamentals of constrained text generation with Outlines - install, configure, and generate your first structured outputs.
 
 ## Installation
@@ -427,4 +430,49 @@ Now that you understand the basics of constrained generation, let's explore:
 - [ ] Implement error handling
 - [ ] Try batch processing for performance
 
-You're now ready to add structure and reliability to your LLM outputs! 🚀
\ No newline at end of file
+You're now ready to add structure and reliability to your LLM outputs! 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `model`, `models`, `outlines` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Outlines` as an operating subsystem inside **Outlines Tutorial: Structured Text Generation with LLMs**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `generator`, `generate`, `print` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Outlines` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `model`.
+2. **Input normalization**: shape incoming data so `models` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `outlines`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/outlines-dev/outlines)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `model` and `models` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Text Patterns & Regular Expressions](02-text-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/outlines-tutorial/02-text-patterns.md b/tutorials/outlines-tutorial/02-text-patterns.md
index 00852659..8672bcee 100644
--- a/tutorials/outlines-tutorial/02-text-patterns.md
+++ b/tutorials/outlines-tutorial/02-text-patterns.md
@@ -8,6 +8,9 @@ parent: Outlines Tutorial
 
 # Chapter 2: Text Patterns & Regular Expressions
 
+Welcome to **Chapter 2: Text Patterns & Regular Expressions**. In this part of **Outlines Tutorial: Structured Text Generation with LLMs**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master regex-based constrained generation - from simple patterns to complex string validation with guaranteed compliance.
 
 ## Basic Regex Constraints
@@ -549,4 +552,50 @@ Sample Results:")
 compare_generation_methods()
 ```
 
-This comprehensive guide to text patterns and regular expressions shows how Outlines can enforce complex string constraints while maintaining high performance. The next chapter explores JSON Schema validation for structured data generation. 🚀
\ No newline at end of file
+This comprehensive guide to text patterns and regular expressions shows how Outlines can enforce complex string constraints while maintaining high performance. The next chapter explores JSON Schema validation for structured data generation. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `pattern`, `self`, `print` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Text Patterns & Regular Expressions` as an operating subsystem inside **Outlines Tutorial: Structured Text Generation with LLMs**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `model`, `Generate`, `result` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Text Patterns & Regular Expressions` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `pattern`.
+2. **Input normalization**: shape incoming data so `self` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `print`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/outlines-dev/outlines)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `pattern` and `self` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Outlines](01-getting-started.md)
+- [Next Chapter: Chapter 3: JSON Schema & Structured Data Generation](03-json-schema.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/outlines-tutorial/03-json-schema.md b/tutorials/outlines-tutorial/03-json-schema.md
index eed8f839..14ee736b 100644
--- a/tutorials/outlines-tutorial/03-json-schema.md
+++ b/tutorials/outlines-tutorial/03-json-schema.md
@@ -8,6 +8,9 @@ parent: Outlines Tutorial
 
 # Chapter 3: JSON Schema & Structured Data Generation
 
+Welcome to **Chapter 3: JSON Schema & Structured Data Generation**. In this part of **Outlines Tutorial: Structured Text Generation with LLMs**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Generate perfectly structured JSON data with guaranteed schema compliance using Outlines JSON Schema support.
 
 ## Basic JSON Schema Generation
@@ -631,4 +634,50 @@ if debug_result:
     print("Final result:", json.dumps(debug_result, indent=2))
 ```
 
-This chapter demonstrates how Outlines can generate complex, schema-compliant JSON data with guaranteed structure and validation. The next chapter covers Pydantic models and type safety. 🚀
\ No newline at end of file
+This chapter demonstrates how Outlines can generate complex, schema-compliant JSON data with guaranteed structure and validation. The next chapter covers Pydantic models and type safety. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `print`, `json`, `Generate` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: JSON Schema & Structured Data Generation` as an operating subsystem inside **Outlines Tutorial: Structured Text Generation with LLMs**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `model`, `schema`, `object` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: JSON Schema & Structured Data Generation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `print`.
+2. **Input normalization**: shape incoming data so `json` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Generate`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/outlines-dev/outlines)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `print` and `json` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Text Patterns & Regular Expressions](02-text-patterns.md)
+- [Next Chapter: Chapter 4: Type Safety & Pydantic Integration](04-type-safety.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/outlines-tutorial/04-type-safety.md b/tutorials/outlines-tutorial/04-type-safety.md
index 321670c3..29d12701 100644
--- a/tutorials/outlines-tutorial/04-type-safety.md
+++ b/tutorials/outlines-tutorial/04-type-safety.md
@@ -8,6 +8,9 @@ parent: Outlines Tutorial
 
 # Chapter 4: Type Safety & Pydantic Integration
 
+Welcome to **Chapter 4: Type Safety & Pydantic Integration**. In this part of **Outlines Tutorial: Structured Text Generation with LLMs**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Generate type-safe Python objects with runtime validation using Pydantic models and Outlines.
 
 ## Basic Pydantic Integration
@@ -691,4 +694,50 @@ except RuntimeError as e:
     print("Generation failed:", str(e))
 ```
 
-This comprehensive chapter demonstrates how Outlines integrates with Pydantic to provide type-safe, validated object generation with runtime guarantees. The next chapter covers context-free grammars and formal language generation. 🚀
\ No newline at end of file
+This comprehensive chapter demonstrates how Outlines integrates with Pydantic to provide type-safe, validated object generation with runtime guarantees. The next chapter covers context-free grammars and formal language generation. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `print`, `task` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Type Safety & Pydantic Integration` as an operating subsystem inside **Outlines Tutorial: Structured Text Generation with LLMs**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Generate`, `model`, `BaseModel` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Type Safety & Pydantic Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `print` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `task`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/outlines-dev/outlines)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `print` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: JSON Schema & Structured Data Generation](03-json-schema.md)
+- [Next Chapter: Chapter 5: Grammar-Based Generation & Context-Free Grammars](05-grammar-based.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/outlines-tutorial/05-grammar-based.md b/tutorials/outlines-tutorial/05-grammar-based.md
index 4bc39475..2f934246 100644
--- a/tutorials/outlines-tutorial/05-grammar-based.md
+++ b/tutorials/outlines-tutorial/05-grammar-based.md
@@ -8,6 +8,9 @@ parent: Outlines Tutorial
 
 # Chapter 5: Grammar-Based Generation & Context-Free Grammars
 
+Welcome to **Chapter 5: Grammar-Based Generation & Context-Free Grammars**. In this part of **Outlines Tutorial: Structured Text Generation with LLMs**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Control LLM outputs with formal grammars - generate syntactically correct programs, mathematical expressions, and domain-specific languages.
 
 ## Basic Context-Free Grammar
@@ -661,4 +664,50 @@ print(f"Optimized: {optimized_time:.3f}s per generation")
 print(f"Improvement: {(original_time - optimized_time) / original_time * 100:.1f}%")
 ```
 
-This comprehensive chapter demonstrates how Outlines uses context-free grammars to generate syntactically correct outputs for programming languages, mathematical expressions, and domain-specific languages. The next chapter covers advanced features and performance optimization. 🚀
\ No newline at end of file
+This comprehensive chapter demonstrates how Outlines uses context-free grammars to generate syntactically correct outputs for programming languages, mathematical expressions, and domain-specific languages. The next chapter covers advanced features and performance optimization. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `expression`, `print`, `grammar` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Grammar-Based Generation & Context-Free Grammars` as an operating subsystem inside **Outlines Tutorial: Structured Text Generation with LLMs**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `self`, `line`, `model` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Grammar-Based Generation & Context-Free Grammars` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `expression`.
+2. **Input normalization**: shape incoming data so `print` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `grammar`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/outlines-dev/outlines)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `expression` and `print` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Type Safety & Pydantic Integration](04-type-safety.md)
+- [Next Chapter: Chapter 6: Advanced Features & Performance Optimization](06-advanced-features.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/outlines-tutorial/06-advanced-features.md b/tutorials/outlines-tutorial/06-advanced-features.md
index 13b489a1..d662a8b1 100644
--- a/tutorials/outlines-tutorial/06-advanced-features.md
+++ b/tutorials/outlines-tutorial/06-advanced-features.md
@@ -8,6 +8,9 @@ parent: Outlines Tutorial
 
 # Chapter 6: Advanced Features & Performance Optimization
 
+Welcome to **Chapter 6: Advanced Features & Performance Optimization**. In this part of **Outlines Tutorial: Structured Text Generation with LLMs**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master advanced Outlines features - custom sampling, performance tuning, batch processing, and enterprise-grade optimization techniques.
 
 ## Advanced Sampling Strategies
@@ -828,4 +831,50 @@ print("Metrics sample:")
 print(metrics.get_metrics_text()[:500] + "...")
 ```
 
-This advanced features chapter demonstrates sophisticated sampling strategies, performance optimizations, batch processing, and enterprise-grade monitoring capabilities that make Outlines suitable for production deployment. The next chapter covers integration with popular frameworks. 🚀
\ No newline at end of file
+This advanced features chapter demonstrates sophisticated sampling strategies, performance optimizations, batch processing, and enterprise-grade monitoring capabilities that make Outlines suitable for production deployment. The next chapter covers integration with popular frameworks. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `model`, `result` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Advanced Features & Performance Optimization` as an operating subsystem inside **Outlines Tutorial: Structured Text Generation with LLMs**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `torch`, `prompt`, `print` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Advanced Features & Performance Optimization` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `result`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/outlines-dev/outlines)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Grammar-Based Generation & Context-Free Grammars](05-grammar-based.md)
+- [Next Chapter: Chapter 7: Integration with AI Frameworks](07-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/outlines-tutorial/07-integration.md b/tutorials/outlines-tutorial/07-integration.md
index 4e7355c5..d1342769 100644
--- a/tutorials/outlines-tutorial/07-integration.md
+++ b/tutorials/outlines-tutorial/07-integration.md
@@ -8,6 +8,9 @@ parent: Outlines Tutorial
 
 # Chapter 7: Integration with AI Frameworks
 
+Welcome to **Chapter 7: Integration with AI Frameworks**. In this part of **Outlines Tutorial: Structured Text Generation with LLMs**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Seamlessly integrate Outlines constrained generation with LangChain, CrewAI, LlamaIndex, and other popular AI frameworks for production-ready applications.
 
 ## LangChain Integration
@@ -853,4 +856,50 @@ agent = crewai_agent_class(
 )
 ```
 
-This comprehensive integration chapter shows how Outlines can be seamlessly integrated with popular AI frameworks, enabling structured generation in complex applications. The next chapter covers production deployment and scaling. 🚀
\ No newline at end of file
+This comprehensive integration chapter shows how Outlines can be seamlessly integrated with popular AI frameworks, enabling structured generation in complex applications. The next chapter covers production deployment and scaling. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `constraint_type`, `constraint_config` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Integration with AI Frameworks` as an operating subsystem inside **Outlines Tutorial: Structured Text Generation with LLMs**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `model_name`, `result`, `generator` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Integration with AI Frameworks` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `constraint_type` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `constraint_config`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/outlines-dev/outlines)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `constraint_type` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Advanced Features & Performance Optimization](06-advanced-features.md)
+- [Next Chapter: Chapter 8: Production Deployment & Scaling](08-production.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/outlines-tutorial/08-production.md b/tutorials/outlines-tutorial/08-production.md
index 36c62d59..f6740a92 100644
--- a/tutorials/outlines-tutorial/08-production.md
+++ b/tutorials/outlines-tutorial/08-production.md
@@ -8,6 +8,9 @@ parent: Outlines Tutorial
 
 # Chapter 8: Production Deployment & Scaling
 
+Welcome to **Chapter 8: Production Deployment & Scaling**. In this part of **Outlines Tutorial: Structured Text Generation with LLMs**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy Outlines constrained generation systems at enterprise scale with high availability, monitoring, and performance optimization.
 
 ## Production Architecture
@@ -1318,4 +1321,49 @@ curl https://your-domain.com/metrics
 python benchmark.py
 ```
 
-This completes the comprehensive Outlines production deployment guide! 🎊
\ No newline at end of file
+This completes the comprehensive Outlines production deployment guide! 🎊
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `model_name`, `model` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment & Scaling` as an operating subsystem inside **Outlines Tutorial: Structured Text Generation with LLMs**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `time`, `outlines`, `health` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment & Scaling` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `model_name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/outlines-dev/outlines)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `model_name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Integration with AI Frameworks](07-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/perplexica-tutorial/01-getting-started.md b/tutorials/perplexica-tutorial/01-getting-started.md
index a3ce9347..2f441011 100644
--- a/tutorials/perplexica-tutorial/01-getting-started.md
+++ b/tutorials/perplexica-tutorial/01-getting-started.md
@@ -133,3 +133,48 @@ Congratulations on setting up your AI search engine! In the next chapter, we'll
 ---
 
 *Generated by [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `your`, `Perplexica`, `Configuration` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Perplexica` as an operating subsystem inside **Perplexica Tutorial: AI-Powered Search Engine**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `localhost`, `Clone`, `repository` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Perplexica` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `your`.
+2. **Input normalization**: shape incoming data so `Perplexica` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Configuration`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ItzCrazyKns/Perplexica)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `your` and `Perplexica` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Search Engine Architecture](02-search-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/perplexica-tutorial/02-search-architecture.md b/tutorials/perplexica-tutorial/02-search-architecture.md
index 0bd9569b..13e1f7f5 100644
--- a/tutorials/perplexica-tutorial/02-search-architecture.md
+++ b/tutorials/perplexica-tutorial/02-search-architecture.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Search Engine Architecture
 
+Welcome to **Chapter 2: Search Engine Architecture**. In this part of **Perplexica Tutorial: AI-Powered Search Engine**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Understanding Perplexica's architecture is key to customizing and extending the search engine. This chapter explores every layer of the system -- from the moment a user types a query to the final synthesized answer with citations. By the end, you will know how each component communicates, where data is stored, and how to extend the architecture for your own use cases.
 
 ## High-Level System Overview
@@ -526,3 +529,49 @@ Now that you understand how the pieces fit together, the next chapter dives deep
 ---
 
 *Built with insights from the [Perplexica](https://github.com/ItzCrazyKns/Perplexica) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `classDef`, `fill`, `stroke` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Search Engine Architecture` as an operating subsystem inside **Perplexica Tutorial: AI-Powered Search Engine**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `config`, `Search`, `search` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Search Engine Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `classDef`.
+2. **Input normalization**: shape incoming data so `fill` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `stroke`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ItzCrazyKns/Perplexica)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `classDef` and `fill` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Perplexica](01-getting-started.md)
+- [Next Chapter: Chapter 3: AI Integration](03-ai-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/perplexica-tutorial/03-ai-integration.md b/tutorials/perplexica-tutorial/03-ai-integration.md
index 884e962e..18b1b9bb 100644
--- a/tutorials/perplexica-tutorial/03-ai-integration.md
+++ b/tutorials/perplexica-tutorial/03-ai-integration.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: AI Integration
 
+Welcome to **Chapter 3: AI Integration**. In this part of **Perplexica Tutorial: AI-Powered Search Engine**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Perplexica's intelligence comes from its seamless integration with large language models. This chapter covers every aspect of connecting to AI providers, configuring models, crafting prompts, and optimizing for cost and quality. You will learn how to add new providers, tune generation parameters, and build the prompt chains that transform raw search results into polished, cited answers.
 
 ## AI Provider Architecture
@@ -505,3 +508,49 @@ With AI integration understood, the next chapter explores how Perplexica gathers
 ---
 
 *Built with insights from the [Perplexica](https://github.com/ItzCrazyKns/Perplexica) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `apiKey`, `models`, `providers` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: AI Integration` as an operating subsystem inside **Perplexica Tutorial: AI-Powered Search Engine**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `model`, `openai`, `temperature` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: AI Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `apiKey`.
+2. **Input normalization**: shape incoming data so `models` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `providers`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ItzCrazyKns/Perplexica)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `apiKey` and `models` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Search Engine Architecture](02-search-architecture.md)
+- [Next Chapter: Chapter 4: Web Scraping and Data Collection](04-web-scraping.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/perplexica-tutorial/04-web-scraping.md b/tutorials/perplexica-tutorial/04-web-scraping.md
index 738ffff8..52b4b47f 100644
--- a/tutorials/perplexica-tutorial/04-web-scraping.md
+++ b/tutorials/perplexica-tutorial/04-web-scraping.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Web Scraping and Data Collection
 
+Welcome to **Chapter 4: Web Scraping and Data Collection**. In this part of **Perplexica Tutorial: AI-Powered Search Engine**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Effective data collection is the foundation of every search answer Perplexica produces. This chapter covers the full pipeline -- from issuing queries to SearXNG, to scraping and parsing individual web pages, to handling file uploads like PDFs and documents. You will learn how each search provider is integrated, how content is extracted and cleaned, and how to add your own data sources.
 
 ## Data Collection Architecture
@@ -611,3 +614,49 @@ With data collection covered, the next chapter explores what happens after the r
 ---
 
 *Built with insights from the [Perplexica](https://github.com/ItzCrazyKns/Perplexica) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `text`, `content`, `subgraph` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Web Scraping and Data Collection` as an operating subsystem inside **Perplexica Tutorial: AI-Powered Search Engine**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `classDef`, `fill`, `stroke` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Web Scraping and Data Collection` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `text`.
+2. **Input normalization**: shape incoming data so `content` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `subgraph`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ItzCrazyKns/Perplexica)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `text` and `content` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: AI Integration](03-ai-integration.md)
+- [Next Chapter: Chapter 5: Result Processing and Ranking](05-result-processing.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/perplexica-tutorial/05-result-processing.md b/tutorials/perplexica-tutorial/05-result-processing.md
index f5848065..57d1b4ed 100644
--- a/tutorials/perplexica-tutorial/05-result-processing.md
+++ b/tutorials/perplexica-tutorial/05-result-processing.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Result Processing and Ranking
 
+Welcome to **Chapter 5: Result Processing and Ranking**. In this part of **Perplexica Tutorial: AI-Powered Search Engine**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 How Perplexica transforms raw search results into coherent, useful answers is the heart of what makes it an AI search engine rather than just another search aggregator. This chapter covers the full result processing pipeline -- deduplication, relevance scoring, embedding-based re-ranking, answer synthesis with inline citations, and quality assurance. By the end, you will understand how to tune every stage for your specific use case.
 
 ## Result Processing Pipeline Overview
@@ -612,3 +615,49 @@ With result processing understood, the next chapter covers how these processed a
 ---
 
 *Built with insights from the [Perplexica](https://github.com/ItzCrazyKns/Perplexica) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `result`, `score`, `results` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Result Processing and Ranking` as an operating subsystem inside **Perplexica Tutorial: AI-Powered Search Engine**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `query`, `subgraph`, `classDef` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Result Processing and Ranking` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `result`.
+2. **Input normalization**: shape incoming data so `score` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `results`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ItzCrazyKns/Perplexica)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `result` and `score` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Web Scraping and Data Collection](04-web-scraping.md)
+- [Next Chapter: Chapter 6: User Interface Development](06-user-interface.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/perplexica-tutorial/06-user-interface.md b/tutorials/perplexica-tutorial/06-user-interface.md
index 8dd21c3d..a1c95a90 100644
--- a/tutorials/perplexica-tutorial/06-user-interface.md
+++ b/tutorials/perplexica-tutorial/06-user-interface.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: User Interface Development
 
+Welcome to **Chapter 6: User Interface Development**. In this part of **Perplexica Tutorial: AI-Powered Search Engine**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Creating an intuitive and powerful search interface is what makes the difference between a backend search API and a product people actually want to use. This chapter covers Perplexica's frontend architecture -- the Next.js application, React component hierarchy, real-time streaming display, theme system, and responsive design. You will learn how every piece of the UI connects to the backend, how to build new components, and how to customize the look and feel.
 
 ## Frontend Architecture Overview
@@ -703,3 +706,49 @@ The UI is the user's window into Perplexica's capabilities. The next chapter exp
 ---
 
 *Built with insights from the [Perplexica](https://github.com/ItzCrazyKns/Perplexica) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `className`, `dark`, `gray` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: User Interface Development` as an operating subsystem inside **Perplexica Tutorial: AI-Powered Search Engine**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `text`, `message`, `theme` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: User Interface Development` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `className`.
+2. **Input normalization**: shape incoming data so `dark` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `gray`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ItzCrazyKns/Perplexica)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `className` and `dark` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Result Processing and Ranking](05-result-processing.md)
+- [Next Chapter: Chapter 7: Advanced Features](07-advanced-features.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/perplexica-tutorial/07-advanced-features.md b/tutorials/perplexica-tutorial/07-advanced-features.md
index 71ce8598..fe851ccb 100644
--- a/tutorials/perplexica-tutorial/07-advanced-features.md
+++ b/tutorials/perplexica-tutorial/07-advanced-features.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Advanced Features
 
+Welcome to **Chapter 7: Advanced Features**. In this part of **Perplexica Tutorial: AI-Powered Search Engine**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Perplexica goes beyond basic search with a rich set of advanced features: multi-turn conversation management, file uploads for document analysis, a REST API for programmatic access, image and video search, and a weather widget. This chapter explores each feature in depth, showing how it is implemented and how you can extend it for your own use cases.
 
 ## Feature Overview
@@ -721,3 +724,49 @@ With all features explored, the final chapter covers the operational side -- how
 ---
 
 *Built with insights from the [Perplexica](https://github.com/ItzCrazyKns/Perplexica) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `json`, `router`, `error` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Advanced Features` as an operating subsystem inside **Perplexica Tutorial: AI-Powered Search Engine**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `chats`, `content`, `text` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Advanced Features` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `json`.
+2. **Input normalization**: shape incoming data so `router` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `error`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ItzCrazyKns/Perplexica)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `json` and `router` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: User Interface Development](06-user-interface.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/perplexica-tutorial/08-production-deployment.md b/tutorials/perplexica-tutorial/08-production-deployment.md
index 3e0e46ae..0ab2e9e9 100644
--- a/tutorials/perplexica-tutorial/08-production-deployment.md
+++ b/tutorials/perplexica-tutorial/08-production-deployment.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **Perplexica Tutorial: AI-Powered Search Engine**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Deploying Perplexica for production use requires careful planning across infrastructure, security, monitoring, and cost management. This chapter covers everything from single-command Docker deployment to multi-instance scaling with load balancing, SSL termination, API key security, observability, and cost optimization. By the end, you will have a production-ready deployment playbook.
 
 ## Deployment Architecture
@@ -722,3 +725,48 @@ You have completed the Perplexica tutorial. You now have the knowledge to instal
 ---
 
 *Built with insights from the [Perplexica](https://github.com/ItzCrazyKns/Perplexica) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `perplexica`, `config`, `searxng` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **Perplexica Tutorial: AI-Powered Search Engine**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `subgraph`, `classDef`, `fill` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `perplexica`.
+2. **Input normalization**: shape incoming data so `config` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `searxng`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ItzCrazyKns/Perplexica)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `perplexica` and `config` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Advanced Features](07-advanced-features.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/phidata-tutorial/01-getting-started.md b/tutorials/phidata-tutorial/01-getting-started.md
index b46a72e3..a344d919 100644
--- a/tutorials/phidata-tutorial/01-getting-started.md
+++ b/tutorials/phidata-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: Phidata Tutorial
 
 # Chapter 1: Getting Started with Phidata Agents
 
+Welcome to **Chapter 1: Getting Started with Phidata Agents**. In this part of **Phidata Tutorial: Building Autonomous AI Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Create your first autonomous AI agent with Phidata - from installation to intelligent conversation.
 
 ## Installation and Setup
@@ -499,4 +502,49 @@ Now that you have created your first Phidata agents, let's explore:
 - [ ] Implement error handling for robustness
 - [ ] Save and load agent configurations
 
-You're now ready to explore the full power of autonomous AI agents! 🚀
\ No newline at end of file
+You're now ready to explore the full power of autonomous AI agents! 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `agent`, `print`, `Agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Phidata Agents` as an operating subsystem inside **Phidata Tutorial: Building Autonomous AI Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `name`, `model`, `instructions` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Phidata Agents` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `agent`.
+2. **Input normalization**: shape incoming data so `print` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/phidatahq/phidata)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `agent` and `print` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Understanding Phidata Agent Architecture](02-agent-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/phidata-tutorial/02-agent-architecture.md b/tutorials/phidata-tutorial/02-agent-architecture.md
index f592078d..7e94c3a5 100644
--- a/tutorials/phidata-tutorial/02-agent-architecture.md
+++ b/tutorials/phidata-tutorial/02-agent-architecture.md
@@ -8,6 +8,9 @@ parent: Phidata Tutorial
 
 # Chapter 2: Understanding Phidata Agent Architecture
 
+Welcome to **Chapter 2: Understanding Phidata Agent Architecture**. In this part of **Phidata Tutorial: Building Autonomous AI Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Dive deep into the internal components, reasoning engine, and modular design that power Phidata agents.
 
 ## Core Agent Components
@@ -869,4 +872,50 @@ status = orchestrator.get_task_status(task_id)
 print(f"Task status: {status['status']}")
 ```
 
-This comprehensive architecture breakdown shows how Phidata agents are composed of modular components that work together to provide intelligent, autonomous behavior. The next chapter explores tools and functions in detail. 🚀
\ No newline at end of file
+This comprehensive architecture breakdown shows how Phidata agents are composed of modular components that work together to provide intelligent, autonomous behavior. The next chapter explores tools and functions in detail. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `agent`, `tool` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Understanding Phidata Agent Architecture` as an operating subsystem inside **Phidata Tutorial: Building Autonomous AI Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Dict`, `tools`, `content` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Understanding Phidata Agent Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `agent` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `tool`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/phidatahq/phidata)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `agent` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Phidata Agents](01-getting-started.md)
+- [Next Chapter: Chapter 3: Tools & Functions - Extending Agent Capabilities](03-tools-functions.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/phidata-tutorial/03-tools-functions.md b/tutorials/phidata-tutorial/03-tools-functions.md
index 90bb5de6..6baad8ba 100644
--- a/tutorials/phidata-tutorial/03-tools-functions.md
+++ b/tutorials/phidata-tutorial/03-tools-functions.md
@@ -8,6 +8,9 @@ parent: Phidata Tutorial
 
 # Chapter 3: Tools & Functions - Extending Agent Capabilities
 
+Welcome to **Chapter 3: Tools & Functions - Extending Agent Capabilities**. In this part of **Phidata Tutorial: Building Autonomous AI Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Equip your Phidata agents with powerful tools and functions for real-world task completion.
 
 ## Built-in Tools
@@ -821,4 +824,50 @@ for query in test_queries:
     print("-" * 50)
 ```
 
-This comprehensive tools and functions chapter demonstrates how to extend Phidata agents with powerful capabilities for real-world task completion. The modular tool system allows for easy integration of custom functionality while maintaining security and reliability. 🚀
\ No newline at end of file
+This comprehensive tools and functions chapter demonstrates how to extend Phidata agents with powerful capabilities for real-world task completion. The modular tool system allows for easy integration of custom functionality while maintaining security and reliability. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `tool`, `result`, `tools` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Tools & Functions - Extending Agent Capabilities` as an operating subsystem inside **Phidata Tutorial: Building Autonomous AI Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `print`, `self`, `name` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Tools & Functions - Extending Agent Capabilities` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `tool`.
+2. **Input normalization**: shape incoming data so `result` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `tools`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/phidatahq/phidata)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `tool` and `result` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Understanding Phidata Agent Architecture](02-agent-architecture.md)
+- [Next Chapter: Chapter 4: Memory Systems - Building Context-Aware Agents](04-memory-systems.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/phidata-tutorial/04-memory-systems.md b/tutorials/phidata-tutorial/04-memory-systems.md
index 0ccd1c31..bfd4abad 100644
--- a/tutorials/phidata-tutorial/04-memory-systems.md
+++ b/tutorials/phidata-tutorial/04-memory-systems.md
@@ -8,6 +8,9 @@ parent: Phidata Tutorial
 
 # Chapter 4: Memory Systems - Building Context-Aware Agents
 
+Welcome to **Chapter 4: Memory Systems - Building Context-Aware Agents**. In this part of **Phidata Tutorial: Building Autonomous AI Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Implement intelligent memory systems that enable agents to maintain context, learn from interactions, and provide personalized experiences.
 
 ## Basic Memory Types
@@ -890,4 +893,50 @@ search_response = indexed_agent.run("What have I said about programming?")
 print(f"Search-based response: {search_response}")
 ```
 
-This comprehensive memory systems chapter demonstrates how to build sophisticated context-aware agents with various memory architectures, from simple buffers to advanced indexed and compressed systems. The modular design allows for easy customization and extension based on specific use cases. 🚀
\ No newline at end of file
+This comprehensive memory systems chapter demonstrates how to build sophisticated context-aware agents with various memory architectures, from simple buffers to advanced indexed and compressed systems. The modular design allows for easy customization and extension based on specific use cases. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `messages`, `memory` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Memory Systems - Building Context-Aware Agents` as an operating subsystem inside **Phidata Tutorial: Building Autonomous AI Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `content`, `print`, `message` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Memory Systems - Building Context-Aware Agents` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `messages` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `memory`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/phidatahq/phidata)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `messages` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Tools & Functions - Extending Agent Capabilities](03-tools-functions.md)
+- [Next Chapter: Chapter 5: Multi-Agent Systems - Coordinating Teams of AI Agents](05-multi-agent-systems.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/phidata-tutorial/05-multi-agent-systems.md b/tutorials/phidata-tutorial/05-multi-agent-systems.md
index 54e0283f..aed05573 100644
--- a/tutorials/phidata-tutorial/05-multi-agent-systems.md
+++ b/tutorials/phidata-tutorial/05-multi-agent-systems.md
@@ -8,6 +8,9 @@ parent: Phidata Tutorial
 
 # Chapter 5: Multi-Agent Systems - Coordinating Teams of AI Agents
 
+Welcome to **Chapter 5: Multi-Agent Systems - Coordinating Teams of AI Agents**. In this part of **Phidata Tutorial: Building Autonomous AI Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Build collaborative agent teams that can delegate tasks, share knowledge, and work together to solve complex problems.
 
 ## Basic Multi-Agent Coordination
@@ -849,4 +852,50 @@ for result in batch_results:
 print(f"\nProcessed {len(batch_results)} tasks successfully")
 ```
 
-This comprehensive multi-agent systems chapter demonstrates how to build collaborative agent teams with sophisticated coordination, communication, and task management capabilities. The modular architecture allows for easy scaling and specialization of agent roles. 🚀
\ No newline at end of file
+This comprehensive multi-agent systems chapter demonstrates how to build collaborative agent teams with sophisticated coordination, communication, and task management capabilities. The modular architecture allows for easy scaling and specialization of agent roles. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `task`, `agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Multi-Agent Systems - Coordinating Teams of AI Agents` as an operating subsystem inside **Phidata Tutorial: Building Autonomous AI Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `message`, `name`, `agents` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Multi-Agent Systems - Coordinating Teams of AI Agents` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `task` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/phidatahq/phidata)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `task` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Memory Systems - Building Context-Aware Agents](04-memory-systems.md)
+- [Next Chapter: Chapter 6: Advanced Reasoning - Complex Decision Making and Problem Solving](06-advanced-reasoning.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/phidata-tutorial/06-advanced-reasoning.md b/tutorials/phidata-tutorial/06-advanced-reasoning.md
index 68680fb5..567302e9 100644
--- a/tutorials/phidata-tutorial/06-advanced-reasoning.md
+++ b/tutorials/phidata-tutorial/06-advanced-reasoning.md
@@ -8,6 +8,9 @@ parent: Phidata Tutorial
 
 # Chapter 6: Advanced Reasoning - Complex Decision Making and Problem Solving
 
+Welcome to **Chapter 6: Advanced Reasoning - Complex Decision Making and Problem Solving**. In this part of **Phidata Tutorial: Building Autonomous AI Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Implement sophisticated reasoning patterns, chain-of-thought processing, and multi-step problem-solving strategies in Phidata agents.
 
 ## Chain-of-Thought Reasoning
@@ -1003,4 +1006,50 @@ for pattern_name in patterns_to_apply:
 print(f"\nAvailable patterns: {list(pattern_library.list_patterns().keys())}")
 ```
 
-This advanced reasoning chapter demonstrates sophisticated reasoning techniques including chain-of-thought, tree-of-thought, self-reflection, and specialized reasoning patterns that enable agents to tackle complex problems systematically. 🚀
\ No newline at end of file
+This advanced reasoning chapter demonstrates sophisticated reasoning techniques including chain-of-thought, tree-of-thought, self-reflection, and specialized reasoning patterns that enable agents to tackle complex problems systematically. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `problem`, `reasoning` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Advanced Reasoning - Complex Decision Making and Problem Solving` as an operating subsystem inside **Phidata Tutorial: Building Autonomous AI Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `confidence`, `line`, `branch` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Advanced Reasoning - Complex Decision Making and Problem Solving` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `problem` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `reasoning`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/phidatahq/phidata)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `problem` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Multi-Agent Systems - Coordinating Teams of AI Agents](05-multi-agent-systems.md)
+- [Next Chapter: Chapter 7: Integrations - Connecting Phidata Agents to External Systems](07-integrations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/phidata-tutorial/07-integrations.md b/tutorials/phidata-tutorial/07-integrations.md
index c1ee880e..005491da 100644
--- a/tutorials/phidata-tutorial/07-integrations.md
+++ b/tutorials/phidata-tutorial/07-integrations.md
@@ -8,6 +8,9 @@ parent: Phidata Tutorial
 
 # Chapter 7: Integrations - Connecting Phidata Agents to External Systems
 
+Welcome to **Chapter 7: Integrations - Connecting Phidata Agents to External Systems**. In this part of **Phidata Tutorial: Building Autonomous AI Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Integrate Phidata agents with databases, APIs, web services, and enterprise systems for comprehensive automation capabilities.
 
 ## Database Integrations
@@ -1034,4 +1037,50 @@ for operation in fs_operations:
     print("-" * 80)
 ```
 
-This comprehensive integrations chapter demonstrates how Phidata agents can connect with databases, APIs, web services, and file systems to perform complex automation tasks across multiple systems. 🚀
\ No newline at end of file
+This comprehensive integrations chapter demonstrates how Phidata agents can connect with databases, APIs, web services, and file systems to perform complex automation tasks across multiple systems. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `headers`, `endpoint` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Integrations - Connecting Phidata Agents to External Systems` as an operating subsystem inside **Phidata Tutorial: Building Autonomous AI Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `response`, `path`, `full_path` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Integrations - Connecting Phidata Agents to External Systems` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `headers` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `endpoint`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/phidatahq/phidata)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `headers` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Advanced Reasoning - Complex Decision Making and Problem Solving](06-advanced-reasoning.md)
+- [Next Chapter: Chapter 8: Production Deployment & Scaling Phidata Agents](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/phidata-tutorial/08-production-deployment.md b/tutorials/phidata-tutorial/08-production-deployment.md
index 87f48f76..ac67d1f8 100644
--- a/tutorials/phidata-tutorial/08-production-deployment.md
+++ b/tutorials/phidata-tutorial/08-production-deployment.md
@@ -8,6 +8,9 @@ parent: Phidata Tutorial
 
 # Chapter 8: Production Deployment & Scaling Phidata Agents
 
+Welcome to **Chapter 8: Production Deployment & Scaling Phidata Agents**. In this part of **Phidata Tutorial: Building Autonomous AI Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy autonomous agent systems at enterprise scale with high availability, monitoring, and production best practices.
 
 ## Production Architecture
@@ -1702,9 +1705,61 @@ curl https://agents.company.com/metrics
 python production_benchmarks.py
 ```
 
-This completes the comprehensive Phidata production deployment guide! 🎊
+This completes the comprehensive Phidata production deployment guide.
+
+## Operational Handoff
+
+Treat this chapter as the production baseline for agent workloads:
+
+- pin model/provider configs by environment and rotate keys on schedule
+- enforce per-tenant rate limits and workload isolation for shared clusters
+- alert on token-cost spikes, latency regressions, and downstream tool failures
+- run disaster recovery drills for vector stores, session stores, and agent memory backends
+- maintain benchmark baselines and rerun after runtime, model, or prompt-stack changes
+
+With these operational controls, Phidata deployments stay predictable under real production load.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `agent_type`, `time` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment & Scaling Phidata Agents` as an operating subsystem inside **Phidata Tutorial: Building Autonomous AI Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `agent`, `user_id`, `duration` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment & Scaling Phidata Agents` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `agent_type` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `time`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/phidatahq/phidata)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `agent_type` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
 
-Now let me commit this work and continue with the next tutorial. I need to update the README and commit the Phidata tutorial. Then I can start on the next one. Let me check what the next tutorial should be - Pydantic AI is next in the list. But first, let me commit what we have. 
+## Chapter Connections
 
-<xai:function_call name="Shell">
-<parameter name="command">cd /Users/johnxie/Documents/GitHub/awesome-code-docs && git add .
\ No newline at end of file
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Integrations - Connecting Phidata Agents to External Systems](07-integrations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/photoprism-tutorial/01-getting-started.md b/tutorials/photoprism-tutorial/01-getting-started.md
index bf70e71a..beaac0e0 100644
--- a/tutorials/photoprism-tutorial/01-getting-started.md
+++ b/tutorials/photoprism-tutorial/01-getting-started.md
@@ -385,3 +385,52 @@ In the next chapter, we'll explore PhotoPrism's AI features and how to configure
 - Web interface is intuitive and feature-rich
 - Basic troubleshooting helps resolve common issues
 - Performance can be tuned based on your hardware
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `photoprism`, `photos`, `docker` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with PhotoPrism` as an operating subsystem inside **PhotoPrism Tutorial: AI-Powered Photos App**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `sudo`, `storage`, `your` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with PhotoPrism` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `photoprism`.
+2. **Input normalization**: shape incoming data so `photos` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `docker`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [github.com/photoprism/photoprism](https://github.com/photoprism/photoprism)
+  Why it matters: authoritative reference on `github.com/photoprism/photoprism` (github.com).
+- [github.com/photoprism/photoprism/discussions](https://github.com/photoprism/photoprism/discussions)
+  Why it matters: authoritative reference on `github.com/photoprism/photoprism/discussions` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `photoprism` and `photos` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: AI Features & Configuration](02-ai-features-configuration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/photoprism-tutorial/02-ai-features-configuration.md b/tutorials/photoprism-tutorial/02-ai-features-configuration.md
index e6a5fdf2..60484301 100644
--- a/tutorials/photoprism-tutorial/02-ai-features-configuration.md
+++ b/tutorials/photoprism-tutorial/02-ai-features-configuration.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: AI Features & Configuration
 
+Welcome to **Chapter 2: AI Features & Configuration**. In this part of **PhotoPrism Tutorial: AI-Powered Photos App**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers PhotoPrism's AI capabilities including TensorFlow integration, automatic tagging, and AI model configuration.
 
 ## 🧠 AI Engine Overview
@@ -246,3 +249,53 @@ const aiMetrics = {
 - Manual review improves AI accuracy over time
 - Performance can be tuned based on hardware
 - AI models are downloaded automatically on first use
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `tags`, `photoprism`, `photos` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: AI Features & Configuration` as an operating subsystem inside **PhotoPrism Tutorial: AI-Powered Photos App**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `workers`, `storage`, `docker` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: AI Features & Configuration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `tags`.
+2. **Input normalization**: shape incoming data so `photoprism` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `photos`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [github.com/photoprism/photoprism](https://github.com/photoprism/photoprism)
+  Why it matters: authoritative reference on `github.com/photoprism/photoprism` (github.com).
+- [github.com/photoprism/photoprism/discussions](https://github.com/photoprism/photoprism/discussions)
+  Why it matters: authoritative reference on `github.com/photoprism/photoprism/discussions` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `tags` and `photoprism` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with PhotoPrism](01-getting-started.md)
+- [Next Chapter: Chapter 3: Photo Management](03-photo-management.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/photoprism-tutorial/03-photo-management.md b/tutorials/photoprism-tutorial/03-photo-management.md
index 1709e0a3..d48ff199 100644
--- a/tutorials/photoprism-tutorial/03-photo-management.md
+++ b/tutorials/photoprism-tutorial/03-photo-management.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Photo Management
 
+Welcome to **Chapter 3: Photo Management**. In this part of **PhotoPrism Tutorial: AI-Powered Photos App**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers importing, organizing, and managing your photo collection in PhotoPrism.
 
 ## 📥 Importing Photos
@@ -261,3 +264,53 @@ const storageOptimization = {
 - Albums provide flexible grouping
 - Quality control ensures good photo management
 - Bulk operations save time with large collections
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `photos`, `albums`, `files` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Photo Management` as an operating subsystem inside **PhotoPrism Tutorial: AI-Powered Photos App**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `specific`, `Photos`, `compression` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Photo Management` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `photos`.
+2. **Input normalization**: shape incoming data so `albums` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `files`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [github.com/photoprism/photoprism](https://github.com/photoprism/photoprism)
+  Why it matters: authoritative reference on `github.com/photoprism/photoprism` (github.com).
+- [github.com/photoprism/photoprism/discussions](https://github.com/photoprism/photoprism/discussions)
+  Why it matters: authoritative reference on `github.com/photoprism/photoprism/discussions` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `photos` and `albums` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: AI Features & Configuration](02-ai-features-configuration.md)
+- [Next Chapter: Chapter 4: Search & Discovery](04-search-discovery.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/photoprism-tutorial/04-search-discovery.md b/tutorials/photoprism-tutorial/04-search-discovery.md
index b6f119ad..8f735edc 100644
--- a/tutorials/photoprism-tutorial/04-search-discovery.md
+++ b/tutorials/photoprism-tutorial/04-search-discovery.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Search & Discovery
 
+Welcome to **Chapter 4: Search & Discovery**. In this part of **PhotoPrism Tutorial: AI-Powered Photos App**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explores PhotoPrism's powerful search capabilities and discovery features for finding photos in your collection.
 
 ## 🔍 Basic Search
@@ -295,3 +298,53 @@ const smartSuggestions = {
 - Facial recognition enables people search
 - Visual search finds similar photos
 - Search can be tuned for performance
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `photos`, `search`, `Photos` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Search & Discovery` as an operating subsystem inside **PhotoPrism Tutorial: AI-Powered Photos App**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `similar`, `beach`, `queries` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Search & Discovery` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `photos`.
+2. **Input normalization**: shape incoming data so `search` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Photos`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [github.com/photoprism/photoprism](https://github.com/photoprism/photoprism)
+  Why it matters: authoritative reference on `github.com/photoprism/photoprism` (github.com).
+- [github.com/photoprism/photoprism/discussions](https://github.com/photoprism/photoprism/discussions)
+  Why it matters: authoritative reference on `github.com/photoprism/photoprism/discussions` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `photos` and `search` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Photo Management](03-photo-management.md)
+- [Next Chapter: Chapter 5: Facial Recognition](05-facial-recognition.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/photoprism-tutorial/05-facial-recognition.md b/tutorials/photoprism-tutorial/05-facial-recognition.md
index 67103b65..26d339ae 100644
--- a/tutorials/photoprism-tutorial/05-facial-recognition.md
+++ b/tutorials/photoprism-tutorial/05-facial-recognition.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Facial Recognition
 
+Welcome to **Chapter 5: Facial Recognition**. In this part of **PhotoPrism Tutorial: AI-Powered Photos App**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers PhotoPrism's facial recognition capabilities for identifying and organizing people in your photos.
 
 ## 👥 Setting Up Facial Recognition
@@ -308,3 +311,53 @@ const personInsights = {
 - Performance can be optimized with hardware
 - Regular maintenance improves results
 - Face data is stored locally and securely
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `photos`, `face`, `person` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Facial Recognition` as an operating subsystem inside **PhotoPrism Tutorial: AI-Powered Photos App**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `faces`, `recognition`, `entries` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Facial Recognition` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `photos`.
+2. **Input normalization**: shape incoming data so `face` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `person`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [github.com/photoprism/photoprism](https://github.com/photoprism/photoprism)
+  Why it matters: authoritative reference on `github.com/photoprism/photoprism` (github.com).
+- [github.com/photoprism/photoprism/discussions](https://github.com/photoprism/photoprism/discussions)
+  Why it matters: authoritative reference on `github.com/photoprism/photoprism/discussions` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `photos` and `face` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Search & Discovery](04-search-discovery.md)
+- [Next Chapter: Chapter 6: API Integration](06-api-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/photoprism-tutorial/06-api-integration.md b/tutorials/photoprism-tutorial/06-api-integration.md
index c3805f07..c70e2f07 100644
--- a/tutorials/photoprism-tutorial/06-api-integration.md
+++ b/tutorials/photoprism-tutorial/06-api-integration.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: API Integration
 
+Welcome to **Chapter 6: API Integration**. In this part of **PhotoPrism Tutorial: AI-Powered Photos App**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers using PhotoPrism's REST API for automation, integration with other services, and custom applications.
 
 ## 🌐 API Overview
@@ -424,3 +427,53 @@ const apiAnalytics = {
 - Security is critical for API access
 - Analytics help monitor usage
 - Integration enables custom workflows
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `apiAuth`, `photos`, `response` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: API Integration` as an operating subsystem inside **PhotoPrism Tutorial: AI-Powered Photos App**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `json`, `password`, `headers` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: API Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `apiAuth`.
+2. **Input normalization**: shape incoming data so `photos` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `response`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [github.com/photoprism/photoprism](https://github.com/photoprism/photoprism)
+  Why it matters: authoritative reference on `github.com/photoprism/photoprism` (github.com).
+- [github.com/photoprism/photoprism/discussions](https://github.com/photoprism/photoprism/discussions)
+  Why it matters: authoritative reference on `github.com/photoprism/photoprism/discussions` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `apiAuth` and `photos` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Facial Recognition](05-facial-recognition.md)
+- [Next Chapter: Chapter 7: Backup & Migration](07-backup-migration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/photoprism-tutorial/07-backup-migration.md b/tutorials/photoprism-tutorial/07-backup-migration.md
index 064a88bc..f5c7c2a0 100644
--- a/tutorials/photoprism-tutorial/07-backup-migration.md
+++ b/tutorials/photoprism-tutorial/07-backup-migration.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Backup & Migration
 
+Welcome to **Chapter 7: Backup & Migration**. In this part of **PhotoPrism Tutorial: AI-Powered Photos App**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers backup strategies, data migration, and disaster recovery for PhotoPrism installations.
 
 ## 💾 Backup Strategies
@@ -446,3 +449,53 @@ const healthChecks = {
 - Monitor backup health continuously
 - Document all procedures thoroughly
 - Consider both cloud and local backup options
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `photoprism`, `docker`, `backup` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Backup & Migration` as an operating subsystem inside **PhotoPrism Tutorial: AI-Powered Photos App**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Backup`, `database`, `exec` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Backup & Migration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `photoprism`.
+2. **Input normalization**: shape incoming data so `docker` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `backup`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [github.com/photoprism/photoprism](https://github.com/photoprism/photoprism)
+  Why it matters: authoritative reference on `github.com/photoprism/photoprism` (github.com).
+- [github.com/photoprism/photoprism/discussions](https://github.com/photoprism/photoprism/discussions)
+  Why it matters: authoritative reference on `github.com/photoprism/photoprism/discussions` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `photoprism` and `docker` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: API Integration](06-api-integration.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/photoprism-tutorial/08-production-deployment.md b/tutorials/photoprism-tutorial/08-production-deployment.md
index 5e7b73f5..fdb75902 100644
--- a/tutorials/photoprism-tutorial/08-production-deployment.md
+++ b/tutorials/photoprism-tutorial/08-production-deployment.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **PhotoPrism Tutorial: AI-Powered Photos App**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This final chapter covers deploying PhotoPrism in production environments with scaling, security, monitoring, and performance optimization.
 
 ## 🚀 Production Architecture
@@ -646,3 +649,52 @@ PRODUCTION_CHECKLIST="
 - Monitoring enables proactive issue resolution
 - Documentation ensures smooth operations
 - Regular maintenance prevents problems
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `photoprism`, `photos`, `example` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **PhotoPrism Tutorial: AI-Powered Photos App**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `storage`, `stats`, `proxy_set_header` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `photoprism`.
+2. **Input normalization**: shape incoming data so `photos` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `example`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [github.com/photoprism/photoprism](https://github.com/photoprism/photoprism)
+  Why it matters: authoritative reference on `github.com/photoprism/photoprism` (github.com).
+- [github.com/photoprism/photoprism/discussions](https://github.com/photoprism/photoprism/discussions)
+  Why it matters: authoritative reference on `github.com/photoprism/photoprism/discussions` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `photoprism` and `photos` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Backup & Migration](07-backup-migration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/plandex-tutorial/01-getting-started.md b/tutorials/plandex-tutorial/01-getting-started.md
index 6acbf2c4..89e1bb96 100644
--- a/tutorials/plandex-tutorial/01-getting-started.md
+++ b/tutorials/plandex-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Plandex Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Plandex Tutorial: Large-Task AI Coding Agent Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Plandex installed and running in a project directory.
 
 ## Quick Install
@@ -31,3 +34,610 @@ curl -sL https://plandex.ai/install.sh | bash
 You now have a functioning Plandex baseline.
 
 Next: [Chapter 2: Architecture and Workflow](02-architecture-and-workflow.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- tutorial slug: **plandex-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Plandex Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Plandex Repository](https://github.com/plandex-ai/plandex)
+- [Plandex Releases](https://github.com/plandex-ai/plandex/releases)
+- [Plandex Docs](https://docs.plandex.ai/)
+- [Plandex Local Self-Hosting Quickstart](https://docs.plandex.ai/hosting/self-hosting/local-mode-quickstart)
+
+### Cross-Tutorial Connection Map
+
+- [Aider Tutorial](../aider-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 1: Getting Started
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `curl`, `https`, `plandex` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Plandex Tutorial: Large-Task AI Coding Agent Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `install`, `bash` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `curl`.
+2. **Input normalization**: shape incoming data so `https` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `plandex`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Plandex Repository](https://github.com/plandex-ai/plandex)
+  Why it matters: authoritative reference on `Plandex Repository` (github.com).
+- [Plandex Releases](https://github.com/plandex-ai/plandex/releases)
+  Why it matters: authoritative reference on `Plandex Releases` (github.com).
+- [Plandex Docs](https://docs.plandex.ai/)
+  Why it matters: authoritative reference on `Plandex Docs` (docs.plandex.ai).
+- [Plandex Local Self-Hosting Quickstart](https://docs.plandex.ai/hosting/self-hosting/local-mode-quickstart)
+  Why it matters: authoritative reference on `Plandex Local Self-Hosting Quickstart` (docs.plandex.ai).
+
+Suggested trace strategy:
+- search upstream code for `curl` and `https` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Architecture and Workflow](02-architecture-and-workflow.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/plandex-tutorial/02-architecture-and-workflow.md b/tutorials/plandex-tutorial/02-architecture-and-workflow.md
index ca585cbc..358ff9ea 100644
--- a/tutorials/plandex-tutorial/02-architecture-and-workflow.md
+++ b/tutorials/plandex-tutorial/02-architecture-and-workflow.md
@@ -7,6 +7,9 @@ parent: Plandex Tutorial
 
 # Chapter 2: Architecture and Workflow
 
+Welcome to **Chapter 2: Architecture and Workflow**. In this part of **Plandex Tutorial: Large-Task AI Coding Agent Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Plandex combines planning, execution, and diff review to support long-horizon coding tasks.
 
 ## Workflow Stages
@@ -21,3 +24,619 @@ Plandex combines planning, execution, and diff review to support long-horizon co
 You now understand Plandex's large-task lifecycle.
 
 Next: [Chapter 3: Context Management at Scale](03-context-management-at-scale.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- tutorial slug: **plandex-tutorial**
+- chapter focus: **Chapter 2: Architecture and Workflow**
+- system context: **Plandex Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Architecture and Workflow`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Plandex Repository](https://github.com/plandex-ai/plandex)
+- [Plandex Releases](https://github.com/plandex-ai/plandex/releases)
+- [Plandex Docs](https://docs.plandex.ai/)
+- [Plandex Local Self-Hosting Quickstart](https://docs.plandex.ai/hosting/self-hosting/local-mode-quickstart)
+
+### Cross-Tutorial Connection Map
+
+- [Aider Tutorial](../aider-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Architecture and Workflow`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 2: Architecture and Workflow
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Architecture and Workflow` as an operating subsystem inside **Plandex Tutorial: Large-Task AI Coding Agent Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Architecture and Workflow` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Plandex Repository](https://github.com/plandex-ai/plandex)
+  Why it matters: authoritative reference on `Plandex Repository` (github.com).
+- [Plandex Releases](https://github.com/plandex-ai/plandex/releases)
+  Why it matters: authoritative reference on `Plandex Releases` (github.com).
+- [Plandex Docs](https://docs.plandex.ai/)
+  Why it matters: authoritative reference on `Plandex Docs` (docs.plandex.ai).
+- [Plandex Local Self-Hosting Quickstart](https://docs.plandex.ai/hosting/self-hosting/local-mode-quickstart)
+  Why it matters: authoritative reference on `Plandex Local Self-Hosting Quickstart` (docs.plandex.ai).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Context Management at Scale](03-context-management-at-scale.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/plandex-tutorial/03-context-management-at-scale.md b/tutorials/plandex-tutorial/03-context-management-at-scale.md
index 284a1195..3f9222c4 100644
--- a/tutorials/plandex-tutorial/03-context-management-at-scale.md
+++ b/tutorials/plandex-tutorial/03-context-management-at-scale.md
@@ -7,6 +7,9 @@ parent: Plandex Tutorial
 
 # Chapter 3: Context Management at Scale
 
+Welcome to **Chapter 3: Context Management at Scale**. In this part of **Plandex Tutorial: Large-Task AI Coding Agent Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Context management is Plandex's core advantage for large files and large codebases.
 
 ## Context Strategy
@@ -22,3 +25,607 @@ Context management is Plandex's core advantage for large files and large codebas
 You now have a context strategy for large-scale tasks in Plandex.
 
 Next: [Chapter 4: Planning, Execution, and Diff Sandbox](04-planning-execution-and-diff-sandbox.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- tutorial slug: **plandex-tutorial**
+- chapter focus: **Chapter 3: Context Management at Scale**
+- system context: **Plandex Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Context Management at Scale`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Plandex Repository](https://github.com/plandex-ai/plandex)
+- [Plandex Releases](https://github.com/plandex-ai/plandex/releases)
+- [Plandex Docs](https://docs.plandex.ai/)
+- [Plandex Local Self-Hosting Quickstart](https://docs.plandex.ai/hosting/self-hosting/local-mode-quickstart)
+
+### Cross-Tutorial Connection Map
+
+- [Aider Tutorial](../aider-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Context Management at Scale`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 3: Context Management at Scale
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Context Management at Scale` as an operating subsystem inside **Plandex Tutorial: Large-Task AI Coding Agent Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Context Management at Scale` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Plandex Repository](https://github.com/plandex-ai/plandex)
+  Why it matters: authoritative reference on `Plandex Repository` (github.com).
+- [Plandex Releases](https://github.com/plandex-ai/plandex/releases)
+  Why it matters: authoritative reference on `Plandex Releases` (github.com).
+- [Plandex Docs](https://docs.plandex.ai/)
+  Why it matters: authoritative reference on `Plandex Docs` (docs.plandex.ai).
+- [Plandex Local Self-Hosting Quickstart](https://docs.plandex.ai/hosting/self-hosting/local-mode-quickstart)
+  Why it matters: authoritative reference on `Plandex Local Self-Hosting Quickstart` (docs.plandex.ai).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Architecture and Workflow](02-architecture-and-workflow.md)
+- [Next Chapter: Chapter 4: Planning, Execution, and Diff Sandbox](04-planning-execution-and-diff-sandbox.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/plandex-tutorial/04-planning-execution-and-diff-sandbox.md b/tutorials/plandex-tutorial/04-planning-execution-and-diff-sandbox.md
index 9bf5c4b2..c0c51835 100644
--- a/tutorials/plandex-tutorial/04-planning-execution-and-diff-sandbox.md
+++ b/tutorials/plandex-tutorial/04-planning-execution-and-diff-sandbox.md
@@ -7,6 +7,9 @@ parent: Plandex Tutorial
 
 # Chapter 4: Planning, Execution, and Diff Sandbox
 
+Welcome to **Chapter 4: Planning, Execution, and Diff Sandbox**. In this part of **Plandex Tutorial: Large-Task AI Coding Agent Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Plandex keeps generated changes separate until they are reviewed, reducing risk in complex tasks.
 
 ## Safety Pattern
@@ -21,3 +24,619 @@ Plandex keeps generated changes separate until they are reviewed, reducing risk
 You now know how to use Plandex's review sandbox for safer high-impact changes.
 
 Next: [Chapter 5: Model Packs and Provider Strategy](05-model-packs-and-provider-strategy.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- tutorial slug: **plandex-tutorial**
+- chapter focus: **Chapter 4: Planning, Execution, and Diff Sandbox**
+- system context: **Plandex Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Planning, Execution, and Diff Sandbox`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Plandex Repository](https://github.com/plandex-ai/plandex)
+- [Plandex Releases](https://github.com/plandex-ai/plandex/releases)
+- [Plandex Docs](https://docs.plandex.ai/)
+- [Plandex Local Self-Hosting Quickstart](https://docs.plandex.ai/hosting/self-hosting/local-mode-quickstart)
+
+### Cross-Tutorial Connection Map
+
+- [Aider Tutorial](../aider-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Planning, Execution, and Diff Sandbox`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 4: Planning, Execution, and Diff Sandbox
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Planning, Execution, and Diff Sandbox` as an operating subsystem inside **Plandex Tutorial: Large-Task AI Coding Agent Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Planning, Execution, and Diff Sandbox` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Plandex Repository](https://github.com/plandex-ai/plandex)
+  Why it matters: authoritative reference on `Plandex Repository` (github.com).
+- [Plandex Releases](https://github.com/plandex-ai/plandex/releases)
+  Why it matters: authoritative reference on `Plandex Releases` (github.com).
+- [Plandex Docs](https://docs.plandex.ai/)
+  Why it matters: authoritative reference on `Plandex Docs` (docs.plandex.ai).
+- [Plandex Local Self-Hosting Quickstart](https://docs.plandex.ai/hosting/self-hosting/local-mode-quickstart)
+  Why it matters: authoritative reference on `Plandex Local Self-Hosting Quickstart` (docs.plandex.ai).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Context Management at Scale](03-context-management-at-scale.md)
+- [Next Chapter: Chapter 5: Model Packs and Provider Strategy](05-model-packs-and-provider-strategy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/plandex-tutorial/05-model-packs-and-provider-strategy.md b/tutorials/plandex-tutorial/05-model-packs-and-provider-strategy.md
index 7b501994..8a1e0f3a 100644
--- a/tutorials/plandex-tutorial/05-model-packs-and-provider-strategy.md
+++ b/tutorials/plandex-tutorial/05-model-packs-and-provider-strategy.md
@@ -7,6 +7,9 @@ parent: Plandex Tutorial
 
 # Chapter 5: Model Packs and Provider Strategy
 
+Welcome to **Chapter 5: Model Packs and Provider Strategy**. In this part of **Plandex Tutorial: Large-Task AI Coding Agent Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Plandex supports combining models across providers to optimize quality, speed, and cost.
 
 ## Strategy Tips
@@ -20,3 +23,619 @@ Plandex supports combining models across providers to optimize quality, speed, a
 You now have a model strategy framework for production Plandex usage.
 
 Next: [Chapter 6: Autonomy, Control, and Debugging](06-autonomy-control-and-debugging.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- tutorial slug: **plandex-tutorial**
+- chapter focus: **Chapter 5: Model Packs and Provider Strategy**
+- system context: **Plandex Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Model Packs and Provider Strategy`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Plandex Repository](https://github.com/plandex-ai/plandex)
+- [Plandex Releases](https://github.com/plandex-ai/plandex/releases)
+- [Plandex Docs](https://docs.plandex.ai/)
+- [Plandex Local Self-Hosting Quickstart](https://docs.plandex.ai/hosting/self-hosting/local-mode-quickstart)
+
+### Cross-Tutorial Connection Map
+
+- [Aider Tutorial](../aider-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Model Packs and Provider Strategy`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 5: Model Packs and Provider Strategy
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Model Packs and Provider Strategy` as an operating subsystem inside **Plandex Tutorial: Large-Task AI Coding Agent Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Model Packs and Provider Strategy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Plandex Repository](https://github.com/plandex-ai/plandex)
+  Why it matters: authoritative reference on `Plandex Repository` (github.com).
+- [Plandex Releases](https://github.com/plandex-ai/plandex/releases)
+  Why it matters: authoritative reference on `Plandex Releases` (github.com).
+- [Plandex Docs](https://docs.plandex.ai/)
+  Why it matters: authoritative reference on `Plandex Docs` (docs.plandex.ai).
+- [Plandex Local Self-Hosting Quickstart](https://docs.plandex.ai/hosting/self-hosting/local-mode-quickstart)
+  Why it matters: authoritative reference on `Plandex Local Self-Hosting Quickstart` (docs.plandex.ai).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Planning, Execution, and Diff Sandbox](04-planning-execution-and-diff-sandbox.md)
+- [Next Chapter: Chapter 6: Autonomy, Control, and Debugging](06-autonomy-control-and-debugging.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/plandex-tutorial/06-autonomy-control-and-debugging.md b/tutorials/plandex-tutorial/06-autonomy-control-and-debugging.md
index 06ea650f..0465ef19 100644
--- a/tutorials/plandex-tutorial/06-autonomy-control-and-debugging.md
+++ b/tutorials/plandex-tutorial/06-autonomy-control-and-debugging.md
@@ -7,6 +7,9 @@ parent: Plandex Tutorial
 
 # Chapter 6: Autonomy, Control, and Debugging
 
+Welcome to **Chapter 6: Autonomy, Control, and Debugging**. In this part of **Plandex Tutorial: Large-Task AI Coding Agent Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Plandex supports both high autonomy and fine-grained control modes depending on task risk.
 
 ## Control Spectrum
@@ -21,3 +24,619 @@ Plandex supports both high autonomy and fine-grained control modes depending on
 You now know how to choose the right autonomy level and debugging posture per task.
 
 Next: [Chapter 7: Git, Branching, and Review Workflows](07-git-branching-and-review-workflows.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- tutorial slug: **plandex-tutorial**
+- chapter focus: **Chapter 6: Autonomy, Control, and Debugging**
+- system context: **Plandex Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Autonomy, Control, and Debugging`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Plandex Repository](https://github.com/plandex-ai/plandex)
+- [Plandex Releases](https://github.com/plandex-ai/plandex/releases)
+- [Plandex Docs](https://docs.plandex.ai/)
+- [Plandex Local Self-Hosting Quickstart](https://docs.plandex.ai/hosting/self-hosting/local-mode-quickstart)
+
+### Cross-Tutorial Connection Map
+
+- [Aider Tutorial](../aider-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Autonomy, Control, and Debugging`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 6: Autonomy, Control, and Debugging
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Autonomy, Control, and Debugging` as an operating subsystem inside **Plandex Tutorial: Large-Task AI Coding Agent Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Autonomy, Control, and Debugging` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Plandex Repository](https://github.com/plandex-ai/plandex)
+  Why it matters: authoritative reference on `Plandex Repository` (github.com).
+- [Plandex Releases](https://github.com/plandex-ai/plandex/releases)
+  Why it matters: authoritative reference on `Plandex Releases` (github.com).
+- [Plandex Docs](https://docs.plandex.ai/)
+  Why it matters: authoritative reference on `Plandex Docs` (docs.plandex.ai).
+- [Plandex Local Self-Hosting Quickstart](https://docs.plandex.ai/hosting/self-hosting/local-mode-quickstart)
+  Why it matters: authoritative reference on `Plandex Local Self-Hosting Quickstart` (docs.plandex.ai).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Model Packs and Provider Strategy](05-model-packs-and-provider-strategy.md)
+- [Next Chapter: Chapter 7: Git, Branching, and Review Workflows](07-git-branching-and-review-workflows.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/plandex-tutorial/07-git-branching-and-review-workflows.md b/tutorials/plandex-tutorial/07-git-branching-and-review-workflows.md
index 24b2eac1..c52ca243 100644
--- a/tutorials/plandex-tutorial/07-git-branching-and-review-workflows.md
+++ b/tutorials/plandex-tutorial/07-git-branching-and-review-workflows.md
@@ -7,6 +7,9 @@ parent: Plandex Tutorial
 
 # Chapter 7: Git, Branching, and Review Workflows
 
+Welcome to **Chapter 7: Git, Branching, and Review Workflows**. In this part of **Plandex Tutorial: Large-Task AI Coding Agent Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Plandex integrates with Git-oriented team workflows for reviewable and reversible delivery.
 
 ## Team Workflow Pattern
@@ -21,3 +24,619 @@ Plandex integrates with Git-oriented team workflows for reviewable and reversibl
 You now have a repeatable review workflow for team-scale Plandex adoption.
 
 Next: [Chapter 8: Self-Hosting and Production Operations](08-self-hosting-and-production-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- tutorial slug: **plandex-tutorial**
+- chapter focus: **Chapter 7: Git, Branching, and Review Workflows**
+- system context: **Plandex Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Git, Branching, and Review Workflows`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Plandex Repository](https://github.com/plandex-ai/plandex)
+- [Plandex Releases](https://github.com/plandex-ai/plandex/releases)
+- [Plandex Docs](https://docs.plandex.ai/)
+- [Plandex Local Self-Hosting Quickstart](https://docs.plandex.ai/hosting/self-hosting/local-mode-quickstart)
+
+### Cross-Tutorial Connection Map
+
+- [Aider Tutorial](../aider-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Git, Branching, and Review Workflows`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 7: Git, Branching, and Review Workflows
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Git, Branching, and Review Workflows` as an operating subsystem inside **Plandex Tutorial: Large-Task AI Coding Agent Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Git, Branching, and Review Workflows` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Plandex Repository](https://github.com/plandex-ai/plandex)
+  Why it matters: authoritative reference on `Plandex Repository` (github.com).
+- [Plandex Releases](https://github.com/plandex-ai/plandex/releases)
+  Why it matters: authoritative reference on `Plandex Releases` (github.com).
+- [Plandex Docs](https://docs.plandex.ai/)
+  Why it matters: authoritative reference on `Plandex Docs` (docs.plandex.ai).
+- [Plandex Local Self-Hosting Quickstart](https://docs.plandex.ai/hosting/self-hosting/local-mode-quickstart)
+  Why it matters: authoritative reference on `Plandex Local Self-Hosting Quickstart` (docs.plandex.ai).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Autonomy, Control, and Debugging](06-autonomy-control-and-debugging.md)
+- [Next Chapter: Chapter 8: Self-Hosting and Production Operations](08-self-hosting-and-production-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/plandex-tutorial/08-self-hosting-and-production-operations.md b/tutorials/plandex-tutorial/08-self-hosting-and-production-operations.md
index 3e7f8cf5..5dc4dd4e 100644
--- a/tutorials/plandex-tutorial/08-self-hosting-and-production-operations.md
+++ b/tutorials/plandex-tutorial/08-self-hosting-and-production-operations.md
@@ -7,6 +7,9 @@ parent: Plandex Tutorial
 
 # Chapter 8: Self-Hosting and Production Operations
 
+Welcome to **Chapter 8: Self-Hosting and Production Operations**. In this part of **Plandex Tutorial: Large-Task AI Coding Agent Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers local/self-hosted operation patterns for production-grade Plandex usage.
 
 ## Operations Checklist
@@ -23,3 +26,606 @@ This chapter covers local/self-hosted operation patterns for production-grade Pl
 ## Summary
 
 You now have an operations baseline for running Plandex as a serious engineering tool.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- tutorial slug: **plandex-tutorial**
+- chapter focus: **Chapter 8: Self-Hosting and Production Operations**
+- system context: **Plandex Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Self-Hosting and Production Operations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Plandex Repository](https://github.com/plandex-ai/plandex)
+- [Plandex Releases](https://github.com/plandex-ai/plandex/releases)
+- [Plandex Docs](https://docs.plandex.ai/)
+- [Plandex Local Self-Hosting Quickstart](https://docs.plandex.ai/hosting/self-hosting/local-mode-quickstart)
+
+### Cross-Tutorial Connection Map
+
+- [Aider Tutorial](../aider-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Self-Hosting and Production Operations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 8: Self-Hosting and Production Operations
+
+- tutorial context: **Plandex Tutorial: Large-Task AI Coding Agent Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Self-Hosting and Production Operations` as an operating subsystem inside **Plandex Tutorial: Large-Task AI Coding Agent Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Self-Hosting and Production Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Plandex Repository](https://github.com/plandex-ai/plandex)
+  Why it matters: authoritative reference on `Plandex Repository` (github.com).
+- [Plandex Releases](https://github.com/plandex-ai/plandex/releases)
+  Why it matters: authoritative reference on `Plandex Releases` (github.com).
+- [Plandex Docs](https://docs.plandex.ai/)
+  Why it matters: authoritative reference on `Plandex Docs` (docs.plandex.ai).
+- [Plandex Local Self-Hosting Quickstart](https://docs.plandex.ai/hosting/self-hosting/local-mode-quickstart)
+  Why it matters: authoritative reference on `Plandex Local Self-Hosting Quickstart` (docs.plandex.ai).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Git, Branching, and Review Workflows](07-git-branching-and-review-workflows.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/planning-with-files-tutorial/01-getting-started.md b/tutorials/planning-with-files-tutorial/01-getting-started.md
index b384f8cc..a1b68b15 100644
--- a/tutorials/planning-with-files-tutorial/01-getting-started.md
+++ b/tutorials/planning-with-files-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Planning with Files Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets the skill installed and running in Claude Code quickly.
 
 ## Learning Goals
@@ -48,3 +51,589 @@ Use one of:
 You now have the baseline workflow installed and active.
 
 Next: [Chapter 2: Core Philosophy and the 3-File Pattern](02-core-philosophy-and-the-3-file-pattern.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- tutorial slug: **planning-with-files-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Planning With Files Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Planning with Files Repository](https://github.com/OthmanAdi/planning-with-files)
+- [README](https://github.com/OthmanAdi/planning-with-files/blob/master/README.md)
+- [Installation Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/installation.md)
+- [Workflow Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/workflow.md)
+- [Troubleshooting Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/troubleshooting.md)
+
+### Cross-Tutorial Connection Map
+
+- [Beads Tutorial](../beads-tutorial/)
+- [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `planning`, `files`, `plugin` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `marketplace`, `OthmanAdi`, `install` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `planning`.
+2. **Input normalization**: shape incoming data so `files` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `plugin`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Planning with Files Repository](https://github.com/OthmanAdi/planning-with-files)
+  Why it matters: authoritative reference on `Planning with Files Repository` (github.com).
+- [README](https://github.com/OthmanAdi/planning-with-files/blob/master/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Installation Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/installation.md)
+  Why it matters: authoritative reference on `Installation Guide` (github.com).
+- [Workflow Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/workflow.md)
+  Why it matters: authoritative reference on `Workflow Guide` (github.com).
+- [Troubleshooting Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/troubleshooting.md)
+  Why it matters: authoritative reference on `Troubleshooting Guide` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `planning` and `files` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Core Philosophy and the 3-File Pattern](02-core-philosophy-and-the-3-file-pattern.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/planning-with-files-tutorial/02-core-philosophy-and-the-3-file-pattern.md b/tutorials/planning-with-files-tutorial/02-core-philosophy-and-the-3-file-pattern.md
index 8d987e73..a7a525eb 100644
--- a/tutorials/planning-with-files-tutorial/02-core-philosophy-and-the-3-file-pattern.md
+++ b/tutorials/planning-with-files-tutorial/02-core-philosophy-and-the-3-file-pattern.md
@@ -7,6 +7,9 @@ parent: Planning with Files Tutorial
 
 # Chapter 2: Core Philosophy and the 3-File Pattern
 
+Welcome to **Chapter 2: Core Philosophy and the 3-File Pattern**. In this part of **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains why durable file memory improves agent execution quality.
 
 ## Learning Goals
@@ -37,3 +40,598 @@ Treat context as RAM and files as disk: anything important must be persisted.
 You now understand the planning model that keeps long-running tasks stable.
 
 Next: [Chapter 3: Installation Paths Across IDEs and Agents](03-installation-paths-across-ides-and-agents.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- tutorial slug: **planning-with-files-tutorial**
+- chapter focus: **Chapter 2: Core Philosophy and the 3-File Pattern**
+- system context: **Planning With Files Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Core Philosophy and the 3-File Pattern`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Planning with Files Repository](https://github.com/OthmanAdi/planning-with-files)
+- [README](https://github.com/OthmanAdi/planning-with-files/blob/master/README.md)
+- [Installation Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/installation.md)
+- [Workflow Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/workflow.md)
+- [Troubleshooting Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/troubleshooting.md)
+
+### Cross-Tutorial Connection Map
+
+- [Beads Tutorial](../beads-tutorial/)
+- [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Core Philosophy and the 3-File Pattern`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Core Philosophy and the 3-File Pattern
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Core Philosophy and the 3-File Pattern` as an operating subsystem inside **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Core Philosophy and the 3-File Pattern` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Planning with Files Repository](https://github.com/OthmanAdi/planning-with-files)
+  Why it matters: authoritative reference on `Planning with Files Repository` (github.com).
+- [README](https://github.com/OthmanAdi/planning-with-files/blob/master/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Installation Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/installation.md)
+  Why it matters: authoritative reference on `Installation Guide` (github.com).
+- [Workflow Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/workflow.md)
+  Why it matters: authoritative reference on `Workflow Guide` (github.com).
+- [Troubleshooting Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/troubleshooting.md)
+  Why it matters: authoritative reference on `Troubleshooting Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Installation Paths Across IDEs and Agents](03-installation-paths-across-ides-and-agents.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/planning-with-files-tutorial/03-installation-paths-across-ides-and-agents.md b/tutorials/planning-with-files-tutorial/03-installation-paths-across-ides-and-agents.md
index ddfd035d..962a5946 100644
--- a/tutorials/planning-with-files-tutorial/03-installation-paths-across-ides-and-agents.md
+++ b/tutorials/planning-with-files-tutorial/03-installation-paths-across-ides-and-agents.md
@@ -7,6 +7,9 @@ parent: Planning with Files Tutorial
 
 # Chapter 3: Installation Paths Across IDEs and Agents
 
+Welcome to **Chapter 3: Installation Paths Across IDEs and Agents**. In this part of **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter compares setup options across supported environments.
 
 ## Learning Goals
@@ -38,3 +41,598 @@ The repo provides setup guides for Claude Code, Codex, OpenCode, Gemini CLI, Cur
 You now have a clear multi-environment installation model.
 
 Next: [Chapter 4: Commands, Hooks, and Workflow Orchestration](04-commands-hooks-and-workflow-orchestration.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- tutorial slug: **planning-with-files-tutorial**
+- chapter focus: **Chapter 3: Installation Paths Across IDEs and Agents**
+- system context: **Planning With Files Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Installation Paths Across IDEs and Agents`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Planning with Files Repository](https://github.com/OthmanAdi/planning-with-files)
+- [README](https://github.com/OthmanAdi/planning-with-files/blob/master/README.md)
+- [Installation Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/installation.md)
+- [Workflow Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/workflow.md)
+- [Troubleshooting Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/troubleshooting.md)
+
+### Cross-Tutorial Connection Map
+
+- [Beads Tutorial](../beads-tutorial/)
+- [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Installation Paths Across IDEs and Agents`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Installation Paths Across IDEs and Agents
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Installation Paths Across IDEs and Agents` as an operating subsystem inside **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Installation Paths Across IDEs and Agents` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Planning with Files Repository](https://github.com/OthmanAdi/planning-with-files)
+  Why it matters: authoritative reference on `Planning with Files Repository` (github.com).
+- [README](https://github.com/OthmanAdi/planning-with-files/blob/master/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Installation Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/installation.md)
+  Why it matters: authoritative reference on `Installation Guide` (github.com).
+- [Workflow Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/workflow.md)
+  Why it matters: authoritative reference on `Workflow Guide` (github.com).
+- [Troubleshooting Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/troubleshooting.md)
+  Why it matters: authoritative reference on `Troubleshooting Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Core Philosophy and the 3-File Pattern](02-core-philosophy-and-the-3-file-pattern.md)
+- [Next Chapter: Chapter 4: Commands, Hooks, and Workflow Orchestration](04-commands-hooks-and-workflow-orchestration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/planning-with-files-tutorial/04-commands-hooks-and-workflow-orchestration.md b/tutorials/planning-with-files-tutorial/04-commands-hooks-and-workflow-orchestration.md
index ebcf056e..31ba8efa 100644
--- a/tutorials/planning-with-files-tutorial/04-commands-hooks-and-workflow-orchestration.md
+++ b/tutorials/planning-with-files-tutorial/04-commands-hooks-and-workflow-orchestration.md
@@ -7,6 +7,9 @@ parent: Planning with Files Tutorial
 
 # Chapter 4: Commands, Hooks, and Workflow Orchestration
 
+Welcome to **Chapter 4: Commands, Hooks, and Workflow Orchestration**. In this part of **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains how command entrypoints and hooks enforce planning discipline.
 
 ## Learning Goals
@@ -39,3 +42,598 @@ This chapter explains how command entrypoints and hooks enforce planning discipl
 You now know how orchestration components enforce workflow consistency.
 
 Next: [Chapter 5: Templates, Scripts, and Session Recovery](05-templates-scripts-and-session-recovery.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- tutorial slug: **planning-with-files-tutorial**
+- chapter focus: **Chapter 4: Commands, Hooks, and Workflow Orchestration**
+- system context: **Planning With Files Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Commands, Hooks, and Workflow Orchestration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Planning with Files Repository](https://github.com/OthmanAdi/planning-with-files)
+- [README](https://github.com/OthmanAdi/planning-with-files/blob/master/README.md)
+- [Installation Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/installation.md)
+- [Workflow Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/workflow.md)
+- [Troubleshooting Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/troubleshooting.md)
+
+### Cross-Tutorial Connection Map
+
+- [Beads Tutorial](../beads-tutorial/)
+- [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Commands, Hooks, and Workflow Orchestration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Commands, Hooks, and Workflow Orchestration
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Commands, Hooks, and Workflow Orchestration` as an operating subsystem inside **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Commands, Hooks, and Workflow Orchestration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Planning with Files Repository](https://github.com/OthmanAdi/planning-with-files)
+  Why it matters: authoritative reference on `Planning with Files Repository` (github.com).
+- [README](https://github.com/OthmanAdi/planning-with-files/blob/master/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Installation Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/installation.md)
+  Why it matters: authoritative reference on `Installation Guide` (github.com).
+- [Workflow Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/workflow.md)
+  Why it matters: authoritative reference on `Workflow Guide` (github.com).
+- [Troubleshooting Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/troubleshooting.md)
+  Why it matters: authoritative reference on `Troubleshooting Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Installation Paths Across IDEs and Agents](03-installation-paths-across-ides-and-agents.md)
+- [Next Chapter: Chapter 5: Templates, Scripts, and Session Recovery](05-templates-scripts-and-session-recovery.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/planning-with-files-tutorial/05-templates-scripts-and-session-recovery.md b/tutorials/planning-with-files-tutorial/05-templates-scripts-and-session-recovery.md
index 3609e2b2..b57dc84e 100644
--- a/tutorials/planning-with-files-tutorial/05-templates-scripts-and-session-recovery.md
+++ b/tutorials/planning-with-files-tutorial/05-templates-scripts-and-session-recovery.md
@@ -7,6 +7,9 @@ parent: Planning with Files Tutorial
 
 # Chapter 5: Templates, Scripts, and Session Recovery
 
+Welcome to **Chapter 5: Templates, Scripts, and Session Recovery**. In this part of **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on recovery and repeatability assets.
 
 ## Learning Goals
@@ -37,3 +40,598 @@ Before resuming work, run status and catchup checks, then reconcile plan and pro
 You now have a resilience toolkit for context resets and interrupted sessions.
 
 Next: [Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)](06-multi-ide-adaptation-codex-gemini-opencode-cursor.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- tutorial slug: **planning-with-files-tutorial**
+- chapter focus: **Chapter 5: Templates, Scripts, and Session Recovery**
+- system context: **Planning With Files Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Templates, Scripts, and Session Recovery`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Planning with Files Repository](https://github.com/OthmanAdi/planning-with-files)
+- [README](https://github.com/OthmanAdi/planning-with-files/blob/master/README.md)
+- [Installation Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/installation.md)
+- [Workflow Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/workflow.md)
+- [Troubleshooting Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/troubleshooting.md)
+
+### Cross-Tutorial Connection Map
+
+- [Beads Tutorial](../beads-tutorial/)
+- [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Templates, Scripts, and Session Recovery`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Templates, Scripts, and Session Recovery
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Templates, Scripts, and Session Recovery` as an operating subsystem inside **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Templates, Scripts, and Session Recovery` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Planning with Files Repository](https://github.com/OthmanAdi/planning-with-files)
+  Why it matters: authoritative reference on `Planning with Files Repository` (github.com).
+- [README](https://github.com/OthmanAdi/planning-with-files/blob/master/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Installation Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/installation.md)
+  Why it matters: authoritative reference on `Installation Guide` (github.com).
+- [Workflow Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/workflow.md)
+  Why it matters: authoritative reference on `Workflow Guide` (github.com).
+- [Troubleshooting Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/troubleshooting.md)
+  Why it matters: authoritative reference on `Troubleshooting Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Commands, Hooks, and Workflow Orchestration](04-commands-hooks-and-workflow-orchestration.md)
+- [Next Chapter: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)](06-multi-ide-adaptation-codex-gemini-opencode-cursor.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/planning-with-files-tutorial/06-multi-ide-adaptation-codex-gemini-opencode-cursor.md b/tutorials/planning-with-files-tutorial/06-multi-ide-adaptation-codex-gemini-opencode-cursor.md
index 5048209f..61276a1e 100644
--- a/tutorials/planning-with-files-tutorial/06-multi-ide-adaptation-codex-gemini-opencode-cursor.md
+++ b/tutorials/planning-with-files-tutorial/06-multi-ide-adaptation-codex-gemini-opencode-cursor.md
@@ -7,6 +7,9 @@ parent: Planning with Files Tutorial
 
 # Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
 
+Welcome to **Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)**. In this part of **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter shows how to carry one planning workflow across multiple coding-agent environments.
 
 ## Learning Goals
@@ -34,3 +37,598 @@ This chapter shows how to carry one planning workflow across multiple coding-age
 You now have a practical strategy for multi-IDE workflow consistency.
 
 Next: [Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks](07-troubleshooting-anti-patterns-and-safety-checks.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- tutorial slug: **planning-with-files-tutorial**
+- chapter focus: **Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)**
+- system context: **Planning With Files Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Planning with Files Repository](https://github.com/OthmanAdi/planning-with-files)
+- [README](https://github.com/OthmanAdi/planning-with-files/blob/master/README.md)
+- [Installation Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/installation.md)
+- [Workflow Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/workflow.md)
+- [Troubleshooting Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/troubleshooting.md)
+
+### Cross-Tutorial Connection Map
+
+- [Beads Tutorial](../beads-tutorial/)
+- [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)` as an operating subsystem inside **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Planning with Files Repository](https://github.com/OthmanAdi/planning-with-files)
+  Why it matters: authoritative reference on `Planning with Files Repository` (github.com).
+- [README](https://github.com/OthmanAdi/planning-with-files/blob/master/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Installation Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/installation.md)
+  Why it matters: authoritative reference on `Installation Guide` (github.com).
+- [Workflow Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/workflow.md)
+  Why it matters: authoritative reference on `Workflow Guide` (github.com).
+- [Troubleshooting Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/troubleshooting.md)
+  Why it matters: authoritative reference on `Troubleshooting Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Templates, Scripts, and Session Recovery](05-templates-scripts-and-session-recovery.md)
+- [Next Chapter: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks](07-troubleshooting-anti-patterns-and-safety-checks.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/planning-with-files-tutorial/07-troubleshooting-anti-patterns-and-safety-checks.md b/tutorials/planning-with-files-tutorial/07-troubleshooting-anti-patterns-and-safety-checks.md
index d6ea2102..af9e2f47 100644
--- a/tutorials/planning-with-files-tutorial/07-troubleshooting-anti-patterns-and-safety-checks.md
+++ b/tutorials/planning-with-files-tutorial/07-troubleshooting-anti-patterns-and-safety-checks.md
@@ -7,6 +7,9 @@ parent: Planning with Files Tutorial
 
 # Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
 
+Welcome to **Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks**. In this part of **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers common failures and how to avoid workflow degradation.
 
 ## Learning Goals
@@ -40,3 +43,598 @@ This chapter covers common failures and how to avoid workflow degradation.
 You now have a robust troubleshooting and safety playbook.
 
 Next: [Chapter 8: Contribution Workflow and Team Adoption](08-contribution-workflow-and-team-adoption.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- tutorial slug: **planning-with-files-tutorial**
+- chapter focus: **Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks**
+- system context: **Planning With Files Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Planning with Files Repository](https://github.com/OthmanAdi/planning-with-files)
+- [README](https://github.com/OthmanAdi/planning-with-files/blob/master/README.md)
+- [Installation Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/installation.md)
+- [Workflow Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/workflow.md)
+- [Troubleshooting Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/troubleshooting.md)
+
+### Cross-Tutorial Connection Map
+
+- [Beads Tutorial](../beads-tutorial/)
+- [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks` as an operating subsystem inside **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Planning with Files Repository](https://github.com/OthmanAdi/planning-with-files)
+  Why it matters: authoritative reference on `Planning with Files Repository` (github.com).
+- [README](https://github.com/OthmanAdi/planning-with-files/blob/master/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Installation Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/installation.md)
+  Why it matters: authoritative reference on `Installation Guide` (github.com).
+- [Workflow Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/workflow.md)
+  Why it matters: authoritative reference on `Workflow Guide` (github.com).
+- [Troubleshooting Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/troubleshooting.md)
+  Why it matters: authoritative reference on `Troubleshooting Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Multi-IDE Adaptation (Codex, Gemini, OpenCode, Cursor)](06-multi-ide-adaptation-codex-gemini-opencode-cursor.md)
+- [Next Chapter: Chapter 8: Contribution Workflow and Team Adoption](08-contribution-workflow-and-team-adoption.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/planning-with-files-tutorial/08-contribution-workflow-and-team-adoption.md b/tutorials/planning-with-files-tutorial/08-contribution-workflow-and-team-adoption.md
index d8d98e9c..e65d166a 100644
--- a/tutorials/planning-with-files-tutorial/08-contribution-workflow-and-team-adoption.md
+++ b/tutorials/planning-with-files-tutorial/08-contribution-workflow-and-team-adoption.md
@@ -7,6 +7,9 @@ parent: Planning with Files Tutorial
 
 # Chapter 8: Contribution Workflow and Team Adoption
 
+Welcome to **Chapter 8: Contribution Workflow and Team Adoption**. In this part of **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains how to scale and evolve planning-with-files in teams.
 
 ## Learning Goals
@@ -44,3 +47,597 @@ Next steps:
 - define team-level template quality standards
 - run pilot adoption on one active project
 - contribute one improvement with docs and compatibility notes
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- tutorial slug: **planning-with-files-tutorial**
+- chapter focus: **Chapter 8: Contribution Workflow and Team Adoption**
+- system context: **Planning With Files Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Contribution Workflow and Team Adoption`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Planning with Files Repository](https://github.com/OthmanAdi/planning-with-files)
+- [README](https://github.com/OthmanAdi/planning-with-files/blob/master/README.md)
+- [Installation Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/installation.md)
+- [Workflow Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/workflow.md)
+- [Troubleshooting Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/troubleshooting.md)
+
+### Cross-Tutorial Connection Map
+
+- [Beads Tutorial](../beads-tutorial/)
+- [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Contribution Workflow and Team Adoption`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Contribution Workflow and Team Adoption
+
+- tutorial context: **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Contribution Workflow and Team Adoption` as an operating subsystem inside **Planning with Files Tutorial: Persistent Markdown Workflow Memory for AI Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Contribution Workflow and Team Adoption` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Planning with Files Repository](https://github.com/OthmanAdi/planning-with-files)
+  Why it matters: authoritative reference on `Planning with Files Repository` (github.com).
+- [README](https://github.com/OthmanAdi/planning-with-files/blob/master/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Installation Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/installation.md)
+  Why it matters: authoritative reference on `Installation Guide` (github.com).
+- [Workflow Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/workflow.md)
+  Why it matters: authoritative reference on `Workflow Guide` (github.com).
+- [Troubleshooting Guide](https://github.com/OthmanAdi/planning-with-files/blob/master/docs/troubleshooting.md)
+  Why it matters: authoritative reference on `Troubleshooting Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Troubleshooting, Anti-Patterns, and Safety Checks](07-troubleshooting-anti-patterns-and-safety-checks.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/playwright-mcp-tutorial/01-getting-started.md b/tutorials/playwright-mcp-tutorial/01-getting-started.md
index 5f2d4753..25764613 100644
--- a/tutorials/playwright-mcp-tutorial/01-getting-started.md
+++ b/tutorials/playwright-mcp-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Playwright MCP Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Playwright MCP installed and validated with a minimal host configuration.
 
 ## Learning Goals
@@ -45,3 +48,589 @@ This chapter gets Playwright MCP installed and validated with a minimal host con
 You now have Playwright MCP connected and executing basic browser tasks.
 
 Next: [Chapter 2: Operating Model: Accessibility Snapshots](02-operating-model-accessibility-snapshots.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- tutorial slug: **playwright-mcp-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Playwright Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Playwright MCP Repository](https://github.com/microsoft/playwright-mcp)
+- [README](https://github.com/microsoft/playwright-mcp/blob/main/README.md)
+- [Chrome Extension Guide](https://github.com/microsoft/playwright-mcp/blob/main/packages/extension/README.md)
+- [Playwright MCP Releases](https://github.com/microsoft/playwright-mcp/releases)
+- [Security Policy](https://github.com/microsoft/playwright-mcp/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [Chrome DevTools MCP Tutorial](../chrome-devtools-mcp-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `playwright`, `mcpServers`, `command` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `args`, `latest` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `playwright`.
+2. **Input normalization**: shape incoming data so `mcpServers` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `command`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Playwright MCP Repository](https://github.com/microsoft/playwright-mcp)
+  Why it matters: authoritative reference on `Playwright MCP Repository` (github.com).
+- [README](https://github.com/microsoft/playwright-mcp/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Chrome Extension Guide](https://github.com/microsoft/playwright-mcp/blob/main/packages/extension/README.md)
+  Why it matters: authoritative reference on `Chrome Extension Guide` (github.com).
+- [Playwright MCP Releases](https://github.com/microsoft/playwright-mcp/releases)
+  Why it matters: authoritative reference on `Playwright MCP Releases` (github.com).
+- [Security Policy](https://github.com/microsoft/playwright-mcp/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `playwright` and `mcpServers` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Operating Model: Accessibility Snapshots](02-operating-model-accessibility-snapshots.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/playwright-mcp-tutorial/02-operating-model-accessibility-snapshots.md b/tutorials/playwright-mcp-tutorial/02-operating-model-accessibility-snapshots.md
index b557ced1..15bb5842 100644
--- a/tutorials/playwright-mcp-tutorial/02-operating-model-accessibility-snapshots.md
+++ b/tutorials/playwright-mcp-tutorial/02-operating-model-accessibility-snapshots.md
@@ -7,6 +7,9 @@ parent: Playwright MCP Tutorial
 
 # Chapter 2: Operating Model: Accessibility Snapshots
 
+Welcome to **Chapter 2: Operating Model: Accessibility Snapshots**. In this part of **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains why Playwright MCP emphasizes structured accessibility snapshots instead of image-first control.
 
 ## Learning Goals
@@ -38,3 +41,598 @@ Use `browser_snapshot` as the primary interaction surface, then reference exact
 You now have the core interaction model for deterministic browser automation.
 
 Next: [Chapter 3: Installation Across Host Clients](03-installation-across-host-clients.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- tutorial slug: **playwright-mcp-tutorial**
+- chapter focus: **Chapter 2: Operating Model: Accessibility Snapshots**
+- system context: **Playwright Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Operating Model: Accessibility Snapshots`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Playwright MCP Repository](https://github.com/microsoft/playwright-mcp)
+- [README](https://github.com/microsoft/playwright-mcp/blob/main/README.md)
+- [Chrome Extension Guide](https://github.com/microsoft/playwright-mcp/blob/main/packages/extension/README.md)
+- [Playwright MCP Releases](https://github.com/microsoft/playwright-mcp/releases)
+- [Security Policy](https://github.com/microsoft/playwright-mcp/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [Chrome DevTools MCP Tutorial](../chrome-devtools-mcp-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Operating Model: Accessibility Snapshots`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Operating Model: Accessibility Snapshots
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Operating Model: Accessibility Snapshots` as an operating subsystem inside **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Operating Model: Accessibility Snapshots` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Playwright MCP Repository](https://github.com/microsoft/playwright-mcp)
+  Why it matters: authoritative reference on `Playwright MCP Repository` (github.com).
+- [README](https://github.com/microsoft/playwright-mcp/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Chrome Extension Guide](https://github.com/microsoft/playwright-mcp/blob/main/packages/extension/README.md)
+  Why it matters: authoritative reference on `Chrome Extension Guide` (github.com).
+- [Playwright MCP Releases](https://github.com/microsoft/playwright-mcp/releases)
+  Why it matters: authoritative reference on `Playwright MCP Releases` (github.com).
+- [Security Policy](https://github.com/microsoft/playwright-mcp/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Installation Across Host Clients](03-installation-across-host-clients.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/playwright-mcp-tutorial/03-installation-across-host-clients.md b/tutorials/playwright-mcp-tutorial/03-installation-across-host-clients.md
index d1329ec6..9c76f72c 100644
--- a/tutorials/playwright-mcp-tutorial/03-installation-across-host-clients.md
+++ b/tutorials/playwright-mcp-tutorial/03-installation-across-host-clients.md
@@ -7,6 +7,9 @@ parent: Playwright MCP Tutorial
 
 # Chapter 3: Installation Across Host Clients
 
+Welcome to **Chapter 3: Installation Across Host Clients**. In this part of **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter shows how to reuse one conceptual setup across multiple MCP host clients.
 
 ## Learning Goals
@@ -36,3 +39,598 @@ The upstream README provides setup patterns for Claude, Codex, Cursor, Copilot,
 You now have a host-portable installation strategy for Playwright MCP.
 
 Next: [Chapter 4: Configuration, Capabilities, and Runtime Modes](04-configuration-capabilities-and-runtime-modes.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- tutorial slug: **playwright-mcp-tutorial**
+- chapter focus: **Chapter 3: Installation Across Host Clients**
+- system context: **Playwright Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Installation Across Host Clients`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Playwright MCP Repository](https://github.com/microsoft/playwright-mcp)
+- [README](https://github.com/microsoft/playwright-mcp/blob/main/README.md)
+- [Chrome Extension Guide](https://github.com/microsoft/playwright-mcp/blob/main/packages/extension/README.md)
+- [Playwright MCP Releases](https://github.com/microsoft/playwright-mcp/releases)
+- [Security Policy](https://github.com/microsoft/playwright-mcp/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [Chrome DevTools MCP Tutorial](../chrome-devtools-mcp-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Installation Across Host Clients`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Installation Across Host Clients
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Installation Across Host Clients` as an operating subsystem inside **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Installation Across Host Clients` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Playwright MCP Repository](https://github.com/microsoft/playwright-mcp)
+  Why it matters: authoritative reference on `Playwright MCP Repository` (github.com).
+- [README](https://github.com/microsoft/playwright-mcp/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Chrome Extension Guide](https://github.com/microsoft/playwright-mcp/blob/main/packages/extension/README.md)
+  Why it matters: authoritative reference on `Chrome Extension Guide` (github.com).
+- [Playwright MCP Releases](https://github.com/microsoft/playwright-mcp/releases)
+  Why it matters: authoritative reference on `Playwright MCP Releases` (github.com).
+- [Security Policy](https://github.com/microsoft/playwright-mcp/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Operating Model: Accessibility Snapshots](02-operating-model-accessibility-snapshots.md)
+- [Next Chapter: Chapter 4: Configuration, Capabilities, and Runtime Modes](04-configuration-capabilities-and-runtime-modes.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/playwright-mcp-tutorial/04-configuration-capabilities-and-runtime-modes.md b/tutorials/playwright-mcp-tutorial/04-configuration-capabilities-and-runtime-modes.md
index ec9f6031..e619eedd 100644
--- a/tutorials/playwright-mcp-tutorial/04-configuration-capabilities-and-runtime-modes.md
+++ b/tutorials/playwright-mcp-tutorial/04-configuration-capabilities-and-runtime-modes.md
@@ -7,6 +7,9 @@ parent: Playwright MCP Tutorial
 
 # Chapter 4: Configuration, Capabilities, and Runtime Modes
 
+Welcome to **Chapter 4: Configuration, Capabilities, and Runtime Modes**. In this part of **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers high-impact runtime flags and capability controls.
 
 ## Learning Goals
@@ -35,3 +38,598 @@ This chapter covers high-impact runtime flags and capability controls.
 You now know which configuration levers matter most for stable operation.
 
 Next: [Chapter 5: Profile State, Extension, and Auth Sessions](05-profile-state-extension-and-auth-sessions.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- tutorial slug: **playwright-mcp-tutorial**
+- chapter focus: **Chapter 4: Configuration, Capabilities, and Runtime Modes**
+- system context: **Playwright Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Configuration, Capabilities, and Runtime Modes`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Playwright MCP Repository](https://github.com/microsoft/playwright-mcp)
+- [README](https://github.com/microsoft/playwright-mcp/blob/main/README.md)
+- [Chrome Extension Guide](https://github.com/microsoft/playwright-mcp/blob/main/packages/extension/README.md)
+- [Playwright MCP Releases](https://github.com/microsoft/playwright-mcp/releases)
+- [Security Policy](https://github.com/microsoft/playwright-mcp/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [Chrome DevTools MCP Tutorial](../chrome-devtools-mcp-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Configuration, Capabilities, and Runtime Modes`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Configuration, Capabilities, and Runtime Modes
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Configuration, Capabilities, and Runtime Modes` as an operating subsystem inside **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Configuration, Capabilities, and Runtime Modes` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Playwright MCP Repository](https://github.com/microsoft/playwright-mcp)
+  Why it matters: authoritative reference on `Playwright MCP Repository` (github.com).
+- [README](https://github.com/microsoft/playwright-mcp/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Chrome Extension Guide](https://github.com/microsoft/playwright-mcp/blob/main/packages/extension/README.md)
+  Why it matters: authoritative reference on `Chrome Extension Guide` (github.com).
+- [Playwright MCP Releases](https://github.com/microsoft/playwright-mcp/releases)
+  Why it matters: authoritative reference on `Playwright MCP Releases` (github.com).
+- [Security Policy](https://github.com/microsoft/playwright-mcp/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Installation Across Host Clients](03-installation-across-host-clients.md)
+- [Next Chapter: Chapter 5: Profile State, Extension, and Auth Sessions](05-profile-state-extension-and-auth-sessions.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/playwright-mcp-tutorial/05-profile-state-extension-and-auth-sessions.md b/tutorials/playwright-mcp-tutorial/05-profile-state-extension-and-auth-sessions.md
index f06e62f7..8d34dcc7 100644
--- a/tutorials/playwright-mcp-tutorial/05-profile-state-extension-and-auth-sessions.md
+++ b/tutorials/playwright-mcp-tutorial/05-profile-state-extension-and-auth-sessions.md
@@ -7,6 +7,9 @@ parent: Playwright MCP Tutorial
 
 # Chapter 5: Profile State, Extension, and Auth Sessions
 
+Welcome to **Chapter 5: Profile State, Extension, and Auth Sessions**. In this part of **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains how to handle authenticated browser contexts safely and reliably.
 
 ## Learning Goals
@@ -35,3 +38,598 @@ This chapter explains how to handle authenticated browser contexts safely and re
 You now have a practical model for handling auth/session continuity in browser automation.
 
 Next: [Chapter 6: Standalone and Docker Deployment](06-standalone-and-docker-deployment.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- tutorial slug: **playwright-mcp-tutorial**
+- chapter focus: **Chapter 5: Profile State, Extension, and Auth Sessions**
+- system context: **Playwright Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Profile State, Extension, and Auth Sessions`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Playwright MCP Repository](https://github.com/microsoft/playwright-mcp)
+- [README](https://github.com/microsoft/playwright-mcp/blob/main/README.md)
+- [Chrome Extension Guide](https://github.com/microsoft/playwright-mcp/blob/main/packages/extension/README.md)
+- [Playwright MCP Releases](https://github.com/microsoft/playwright-mcp/releases)
+- [Security Policy](https://github.com/microsoft/playwright-mcp/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [Chrome DevTools MCP Tutorial](../chrome-devtools-mcp-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Profile State, Extension, and Auth Sessions`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Profile State, Extension, and Auth Sessions
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Profile State, Extension, and Auth Sessions` as an operating subsystem inside **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Profile State, Extension, and Auth Sessions` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Playwright MCP Repository](https://github.com/microsoft/playwright-mcp)
+  Why it matters: authoritative reference on `Playwright MCP Repository` (github.com).
+- [README](https://github.com/microsoft/playwright-mcp/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Chrome Extension Guide](https://github.com/microsoft/playwright-mcp/blob/main/packages/extension/README.md)
+  Why it matters: authoritative reference on `Chrome Extension Guide` (github.com).
+- [Playwright MCP Releases](https://github.com/microsoft/playwright-mcp/releases)
+  Why it matters: authoritative reference on `Playwright MCP Releases` (github.com).
+- [Security Policy](https://github.com/microsoft/playwright-mcp/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Configuration, Capabilities, and Runtime Modes](04-configuration-capabilities-and-runtime-modes.md)
+- [Next Chapter: Chapter 6: Standalone and Docker Deployment](06-standalone-and-docker-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/playwright-mcp-tutorial/06-standalone-and-docker-deployment.md b/tutorials/playwright-mcp-tutorial/06-standalone-and-docker-deployment.md
index 46abc58c..80389751 100644
--- a/tutorials/playwright-mcp-tutorial/06-standalone-and-docker-deployment.md
+++ b/tutorials/playwright-mcp-tutorial/06-standalone-and-docker-deployment.md
@@ -7,6 +7,9 @@ parent: Playwright MCP Tutorial
 
 # Chapter 6: Standalone and Docker Deployment
 
+Welcome to **Chapter 6: Standalone and Docker Deployment**. In this part of **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers deployment modes beyond basic stdio invocation.
 
 ## Learning Goals
@@ -35,3 +38,598 @@ This chapter covers deployment modes beyond basic stdio invocation.
 You now have options for scaling Playwright MCP beyond default client-managed execution.
 
 Next: [Chapter 7: Tooling Surface and Automation Patterns](07-tooling-surface-and-automation-patterns.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- tutorial slug: **playwright-mcp-tutorial**
+- chapter focus: **Chapter 6: Standalone and Docker Deployment**
+- system context: **Playwright Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Standalone and Docker Deployment`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Playwright MCP Repository](https://github.com/microsoft/playwright-mcp)
+- [README](https://github.com/microsoft/playwright-mcp/blob/main/README.md)
+- [Chrome Extension Guide](https://github.com/microsoft/playwright-mcp/blob/main/packages/extension/README.md)
+- [Playwright MCP Releases](https://github.com/microsoft/playwright-mcp/releases)
+- [Security Policy](https://github.com/microsoft/playwright-mcp/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [Chrome DevTools MCP Tutorial](../chrome-devtools-mcp-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Standalone and Docker Deployment`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Standalone and Docker Deployment
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Standalone and Docker Deployment` as an operating subsystem inside **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Standalone and Docker Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Playwright MCP Repository](https://github.com/microsoft/playwright-mcp)
+  Why it matters: authoritative reference on `Playwright MCP Repository` (github.com).
+- [README](https://github.com/microsoft/playwright-mcp/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Chrome Extension Guide](https://github.com/microsoft/playwright-mcp/blob/main/packages/extension/README.md)
+  Why it matters: authoritative reference on `Chrome Extension Guide` (github.com).
+- [Playwright MCP Releases](https://github.com/microsoft/playwright-mcp/releases)
+  Why it matters: authoritative reference on `Playwright MCP Releases` (github.com).
+- [Security Policy](https://github.com/microsoft/playwright-mcp/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Profile State, Extension, and Auth Sessions](05-profile-state-extension-and-auth-sessions.md)
+- [Next Chapter: Chapter 7: Tooling Surface and Automation Patterns](07-tooling-surface-and-automation-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/playwright-mcp-tutorial/07-tooling-surface-and-automation-patterns.md b/tutorials/playwright-mcp-tutorial/07-tooling-surface-and-automation-patterns.md
index 3bd242e5..e6655f64 100644
--- a/tutorials/playwright-mcp-tutorial/07-tooling-surface-and-automation-patterns.md
+++ b/tutorials/playwright-mcp-tutorial/07-tooling-surface-and-automation-patterns.md
@@ -7,6 +7,9 @@ parent: Playwright MCP Tutorial
 
 # Chapter 7: Tooling Surface and Automation Patterns
 
+Welcome to **Chapter 7: Tooling Surface and Automation Patterns**. In this part of **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter translates the full tool catalog into reliable automation patterns.
 
 ## Learning Goals
@@ -35,3 +38,598 @@ This chapter translates the full tool catalog into reliable automation patterns.
 You now have a repeatable pattern for stable browser automation loops in agent workflows.
 
 Next: [Chapter 8: Troubleshooting, Security, and Contribution](08-troubleshooting-security-and-contribution.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- tutorial slug: **playwright-mcp-tutorial**
+- chapter focus: **Chapter 7: Tooling Surface and Automation Patterns**
+- system context: **Playwright Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Tooling Surface and Automation Patterns`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Playwright MCP Repository](https://github.com/microsoft/playwright-mcp)
+- [README](https://github.com/microsoft/playwright-mcp/blob/main/README.md)
+- [Chrome Extension Guide](https://github.com/microsoft/playwright-mcp/blob/main/packages/extension/README.md)
+- [Playwright MCP Releases](https://github.com/microsoft/playwright-mcp/releases)
+- [Security Policy](https://github.com/microsoft/playwright-mcp/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [Chrome DevTools MCP Tutorial](../chrome-devtools-mcp-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Tooling Surface and Automation Patterns`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Tooling Surface and Automation Patterns
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Tooling Surface and Automation Patterns` as an operating subsystem inside **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Tooling Surface and Automation Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Playwright MCP Repository](https://github.com/microsoft/playwright-mcp)
+  Why it matters: authoritative reference on `Playwright MCP Repository` (github.com).
+- [README](https://github.com/microsoft/playwright-mcp/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Chrome Extension Guide](https://github.com/microsoft/playwright-mcp/blob/main/packages/extension/README.md)
+  Why it matters: authoritative reference on `Chrome Extension Guide` (github.com).
+- [Playwright MCP Releases](https://github.com/microsoft/playwright-mcp/releases)
+  Why it matters: authoritative reference on `Playwright MCP Releases` (github.com).
+- [Security Policy](https://github.com/microsoft/playwright-mcp/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Standalone and Docker Deployment](06-standalone-and-docker-deployment.md)
+- [Next Chapter: Chapter 8: Troubleshooting, Security, and Contribution](08-troubleshooting-security-and-contribution.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/playwright-mcp-tutorial/08-troubleshooting-security-and-contribution.md b/tutorials/playwright-mcp-tutorial/08-troubleshooting-security-and-contribution.md
index f5a926de..dd572246 100644
--- a/tutorials/playwright-mcp-tutorial/08-troubleshooting-security-and-contribution.md
+++ b/tutorials/playwright-mcp-tutorial/08-troubleshooting-security-and-contribution.md
@@ -7,6 +7,9 @@ parent: Playwright MCP Tutorial
 
 # Chapter 8: Troubleshooting, Security, and Contribution
 
+Welcome to **Chapter 8: Troubleshooting, Security, and Contribution**. In this part of **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers practical troubleshooting and safe evolution of Playwright MCP usage.
 
 ## Learning Goals
@@ -39,3 +42,597 @@ Next steps:
 - standardize one baseline config per host used by your team
 - build one deterministic snapshot-first browser workflow and reuse it
 - audit session and credential handling before broad rollout
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- tutorial slug: **playwright-mcp-tutorial**
+- chapter focus: **Chapter 8: Troubleshooting, Security, and Contribution**
+- system context: **Playwright Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Troubleshooting, Security, and Contribution`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Playwright MCP Repository](https://github.com/microsoft/playwright-mcp)
+- [README](https://github.com/microsoft/playwright-mcp/blob/main/README.md)
+- [Chrome Extension Guide](https://github.com/microsoft/playwright-mcp/blob/main/packages/extension/README.md)
+- [Playwright MCP Releases](https://github.com/microsoft/playwright-mcp/releases)
+- [Security Policy](https://github.com/microsoft/playwright-mcp/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [Chrome DevTools MCP Tutorial](../chrome-devtools-mcp-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Troubleshooting, Security, and Contribution`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Troubleshooting, Security, and Contribution
+
+- tutorial context: **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Troubleshooting, Security, and Contribution` as an operating subsystem inside **Playwright MCP Tutorial: Browser Automation for Coding Agents Through MCP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Troubleshooting, Security, and Contribution` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Playwright MCP Repository](https://github.com/microsoft/playwright-mcp)
+  Why it matters: authoritative reference on `Playwright MCP Repository` (github.com).
+- [README](https://github.com/microsoft/playwright-mcp/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Chrome Extension Guide](https://github.com/microsoft/playwright-mcp/blob/main/packages/extension/README.md)
+  Why it matters: authoritative reference on `Chrome Extension Guide` (github.com).
+- [Playwright MCP Releases](https://github.com/microsoft/playwright-mcp/releases)
+  Why it matters: authoritative reference on `Playwright MCP Releases` (github.com).
+- [Security Policy](https://github.com/microsoft/playwright-mcp/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Tooling Surface and Automation Patterns](07-tooling-surface-and-automation-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/pocketflow-tutorial/01-getting-started.md b/tutorials/pocketflow-tutorial/01-getting-started.md
index ac2d47b5..db270cc3 100644
--- a/tutorials/pocketflow-tutorial/01-getting-started.md
+++ b/tutorials/pocketflow-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: PocketFlow Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets PocketFlow installed and running through a minimal graph workflow.
 
 ## Learning Goals
@@ -32,3 +35,607 @@ pip install pocketflow
 You now have a runnable PocketFlow setup and know where to find core patterns.
 
 Next: [Chapter 2: Core Graph Abstraction](02-core-graph-abstraction.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- tutorial slug: **pocketflow-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Pocketflow Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [PocketFlow Repository](https://github.com/The-Pocket/PocketFlow)
+- [PocketFlow Docs](https://the-pocket.github.io/PocketFlow/)
+- [PocketFlow Cookbook](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook)
+
+### Cross-Tutorial Connection Map
+
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Agno Tutorial](../agno-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 1: Getting Started
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `install`, `pocketflow` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `install`.
+2. **Input normalization**: shape incoming data so `pocketflow` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [PocketFlow Repository](https://github.com/The-Pocket/PocketFlow)
+  Why it matters: authoritative reference on `PocketFlow Repository` (github.com).
+- [PocketFlow Docs](https://the-pocket.github.io/PocketFlow/)
+  Why it matters: authoritative reference on `PocketFlow Docs` (the-pocket.github.io).
+- [PocketFlow Cookbook](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook)
+  Why it matters: authoritative reference on `PocketFlow Cookbook` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `install` and `pocketflow` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Core Graph Abstraction](02-core-graph-abstraction.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/pocketflow-tutorial/02-core-graph-abstraction.md b/tutorials/pocketflow-tutorial/02-core-graph-abstraction.md
index 54763319..44da3950 100644
--- a/tutorials/pocketflow-tutorial/02-core-graph-abstraction.md
+++ b/tutorials/pocketflow-tutorial/02-core-graph-abstraction.md
@@ -7,6 +7,9 @@ parent: PocketFlow Tutorial
 
 # Chapter 2: Core Graph Abstraction
 
+Welcome to **Chapter 2: Core Graph Abstraction**. In this part of **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 PocketFlow's core abstraction is a graph that models control flow and execution transitions.
 
 ## Why Graph First
@@ -20,3 +23,616 @@ PocketFlow's core abstraction is a graph that models control flow and execution
 You now understand how the graph abstraction underpins all PocketFlow capabilities.
 
 Next: [Chapter 3: Agent and Workflow Patterns](03-agent-and-workflow-patterns.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- tutorial slug: **pocketflow-tutorial**
+- chapter focus: **Chapter 2: Core Graph Abstraction**
+- system context: **Pocketflow Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Core Graph Abstraction`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [PocketFlow Repository](https://github.com/The-Pocket/PocketFlow)
+- [PocketFlow Docs](https://the-pocket.github.io/PocketFlow/)
+- [PocketFlow Cookbook](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook)
+
+### Cross-Tutorial Connection Map
+
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Agno Tutorial](../agno-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Core Graph Abstraction`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 2: Core Graph Abstraction
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Core Graph Abstraction` as an operating subsystem inside **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Core Graph Abstraction` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [PocketFlow Repository](https://github.com/The-Pocket/PocketFlow)
+  Why it matters: authoritative reference on `PocketFlow Repository` (github.com).
+- [PocketFlow Docs](https://the-pocket.github.io/PocketFlow/)
+  Why it matters: authoritative reference on `PocketFlow Docs` (the-pocket.github.io).
+- [PocketFlow Cookbook](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook)
+  Why it matters: authoritative reference on `PocketFlow Cookbook` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Agent and Workflow Patterns](03-agent-and-workflow-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/pocketflow-tutorial/03-agent-and-workflow-patterns.md b/tutorials/pocketflow-tutorial/03-agent-and-workflow-patterns.md
index fec53461..860af01b 100644
--- a/tutorials/pocketflow-tutorial/03-agent-and-workflow-patterns.md
+++ b/tutorials/pocketflow-tutorial/03-agent-and-workflow-patterns.md
@@ -7,6 +7,9 @@ parent: PocketFlow Tutorial
 
 # Chapter 3: Agent and Workflow Patterns
 
+Welcome to **Chapter 3: Agent and Workflow Patterns**. In this part of **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 PocketFlow supports agent and workflow designs through reusable graph composition patterns.
 
 ## Pattern Set
@@ -22,3 +25,616 @@ PocketFlow supports agent and workflow designs through reusable graph compositio
 You now have composition patterns for turning simple nodes into full agent workflows.
 
 Next: [Chapter 4: RAG and Knowledge Patterns](04-rag-and-knowledge-patterns.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- tutorial slug: **pocketflow-tutorial**
+- chapter focus: **Chapter 3: Agent and Workflow Patterns**
+- system context: **Pocketflow Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Agent and Workflow Patterns`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [PocketFlow Repository](https://github.com/The-Pocket/PocketFlow)
+- [PocketFlow Docs](https://the-pocket.github.io/PocketFlow/)
+- [PocketFlow Cookbook](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook)
+
+### Cross-Tutorial Connection Map
+
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Agno Tutorial](../agno-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Agent and Workflow Patterns`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 3: Agent and Workflow Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Agent and Workflow Patterns` as an operating subsystem inside **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Agent and Workflow Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [PocketFlow Repository](https://github.com/The-Pocket/PocketFlow)
+  Why it matters: authoritative reference on `PocketFlow Repository` (github.com).
+- [PocketFlow Docs](https://the-pocket.github.io/PocketFlow/)
+  Why it matters: authoritative reference on `PocketFlow Docs` (the-pocket.github.io).
+- [PocketFlow Cookbook](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook)
+  Why it matters: authoritative reference on `PocketFlow Cookbook` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Core Graph Abstraction](02-core-graph-abstraction.md)
+- [Next Chapter: Chapter 4: RAG and Knowledge Patterns](04-rag-and-knowledge-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/pocketflow-tutorial/04-rag-and-knowledge-patterns.md b/tutorials/pocketflow-tutorial/04-rag-and-knowledge-patterns.md
index 09601e06..0a0e8c8c 100644
--- a/tutorials/pocketflow-tutorial/04-rag-and-knowledge-patterns.md
+++ b/tutorials/pocketflow-tutorial/04-rag-and-knowledge-patterns.md
@@ -7,6 +7,9 @@ parent: PocketFlow Tutorial
 
 # Chapter 4: RAG and Knowledge Patterns
 
+Welcome to **Chapter 4: RAG and Knowledge Patterns**. In this part of **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 RAG can be implemented in PocketFlow with explicit retrieval and synthesis node boundaries.
 
 ## RAG Flow
@@ -21,3 +24,616 @@ RAG can be implemented in PocketFlow with explicit retrieval and synthesis node
 You now know how to model retrieval workflows with clear graph boundaries.
 
 Next: [Chapter 5: Multi-Agent and Supervision](05-multi-agent-and-supervision.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- tutorial slug: **pocketflow-tutorial**
+- chapter focus: **Chapter 4: RAG and Knowledge Patterns**
+- system context: **Pocketflow Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: RAG and Knowledge Patterns`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [PocketFlow Repository](https://github.com/The-Pocket/PocketFlow)
+- [PocketFlow Docs](https://the-pocket.github.io/PocketFlow/)
+- [PocketFlow Cookbook](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook)
+
+### Cross-Tutorial Connection Map
+
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Agno Tutorial](../agno-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: RAG and Knowledge Patterns`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 4: RAG and Knowledge Patterns
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: RAG and Knowledge Patterns` as an operating subsystem inside **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: RAG and Knowledge Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [PocketFlow Repository](https://github.com/The-Pocket/PocketFlow)
+  Why it matters: authoritative reference on `PocketFlow Repository` (github.com).
+- [PocketFlow Docs](https://the-pocket.github.io/PocketFlow/)
+  Why it matters: authoritative reference on `PocketFlow Docs` (the-pocket.github.io).
+- [PocketFlow Cookbook](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook)
+  Why it matters: authoritative reference on `PocketFlow Cookbook` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Agent and Workflow Patterns](03-agent-and-workflow-patterns.md)
+- [Next Chapter: Chapter 5: Multi-Agent and Supervision](05-multi-agent-and-supervision.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/pocketflow-tutorial/05-multi-agent-and-supervision.md b/tutorials/pocketflow-tutorial/05-multi-agent-and-supervision.md
index 3fae546f..f0b81c9e 100644
--- a/tutorials/pocketflow-tutorial/05-multi-agent-and-supervision.md
+++ b/tutorials/pocketflow-tutorial/05-multi-agent-and-supervision.md
@@ -7,6 +7,9 @@ parent: PocketFlow Tutorial
 
 # Chapter 5: Multi-Agent and Supervision
 
+Welcome to **Chapter 5: Multi-Agent and Supervision**. In this part of **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Multi-agent and supervisor patterns in PocketFlow are built through graph composition and handoff rules.
 
 ## Reliability Tips
@@ -20,3 +23,616 @@ Multi-agent and supervisor patterns in PocketFlow are built through graph compos
 You now have a baseline for orchestrating multiple agents with supervision loops.
 
 Next: [Chapter 6: Streaming, HITL, and Interrupts](06-streaming-hitl-and-interrupts.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- tutorial slug: **pocketflow-tutorial**
+- chapter focus: **Chapter 5: Multi-Agent and Supervision**
+- system context: **Pocketflow Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Multi-Agent and Supervision`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [PocketFlow Repository](https://github.com/The-Pocket/PocketFlow)
+- [PocketFlow Docs](https://the-pocket.github.io/PocketFlow/)
+- [PocketFlow Cookbook](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook)
+
+### Cross-Tutorial Connection Map
+
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Agno Tutorial](../agno-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Multi-Agent and Supervision`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 5: Multi-Agent and Supervision
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Multi-Agent and Supervision` as an operating subsystem inside **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Multi-Agent and Supervision` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [PocketFlow Repository](https://github.com/The-Pocket/PocketFlow)
+  Why it matters: authoritative reference on `PocketFlow Repository` (github.com).
+- [PocketFlow Docs](https://the-pocket.github.io/PocketFlow/)
+  Why it matters: authoritative reference on `PocketFlow Docs` (the-pocket.github.io).
+- [PocketFlow Cookbook](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook)
+  Why it matters: authoritative reference on `PocketFlow Cookbook` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: RAG and Knowledge Patterns](04-rag-and-knowledge-patterns.md)
+- [Next Chapter: Chapter 6: Streaming, HITL, and Interrupts](06-streaming-hitl-and-interrupts.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/pocketflow-tutorial/06-streaming-hitl-and-interrupts.md b/tutorials/pocketflow-tutorial/06-streaming-hitl-and-interrupts.md
index b0bc1b8b..d34d3057 100644
--- a/tutorials/pocketflow-tutorial/06-streaming-hitl-and-interrupts.md
+++ b/tutorials/pocketflow-tutorial/06-streaming-hitl-and-interrupts.md
@@ -7,6 +7,9 @@ parent: PocketFlow Tutorial
 
 # Chapter 6: Streaming, HITL, and Interrupts
 
+Welcome to **Chapter 6: Streaming, HITL, and Interrupts**. In this part of **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 PocketFlow cookbook patterns cover streaming responses and human-in-the-loop interruption points.
 
 ## Interaction Controls
@@ -22,3 +25,616 @@ PocketFlow cookbook patterns cover streaming responses and human-in-the-loop int
 You now know how to add interactive controls to PocketFlow applications.
 
 Next: [Chapter 7: Multi-Language Ecosystem](07-multi-language-ecosystem.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- tutorial slug: **pocketflow-tutorial**
+- chapter focus: **Chapter 6: Streaming, HITL, and Interrupts**
+- system context: **Pocketflow Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Streaming, HITL, and Interrupts`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [PocketFlow Repository](https://github.com/The-Pocket/PocketFlow)
+- [PocketFlow Docs](https://the-pocket.github.io/PocketFlow/)
+- [PocketFlow Cookbook](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook)
+
+### Cross-Tutorial Connection Map
+
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Agno Tutorial](../agno-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Streaming, HITL, and Interrupts`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 6: Streaming, HITL, and Interrupts
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Streaming, HITL, and Interrupts` as an operating subsystem inside **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Streaming, HITL, and Interrupts` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [PocketFlow Repository](https://github.com/The-Pocket/PocketFlow)
+  Why it matters: authoritative reference on `PocketFlow Repository` (github.com).
+- [PocketFlow Docs](https://the-pocket.github.io/PocketFlow/)
+  Why it matters: authoritative reference on `PocketFlow Docs` (the-pocket.github.io).
+- [PocketFlow Cookbook](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook)
+  Why it matters: authoritative reference on `PocketFlow Cookbook` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Multi-Agent and Supervision](05-multi-agent-and-supervision.md)
+- [Next Chapter: Chapter 7: Multi-Language Ecosystem](07-multi-language-ecosystem.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/pocketflow-tutorial/07-multi-language-ecosystem.md b/tutorials/pocketflow-tutorial/07-multi-language-ecosystem.md
index 5523d060..21c125f7 100644
--- a/tutorials/pocketflow-tutorial/07-multi-language-ecosystem.md
+++ b/tutorials/pocketflow-tutorial/07-multi-language-ecosystem.md
@@ -7,6 +7,9 @@ parent: PocketFlow Tutorial
 
 # Chapter 7: Multi-Language Ecosystem
 
+Welcome to **Chapter 7: Multi-Language Ecosystem**. In this part of **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 PocketFlow has ports across TypeScript, Java, C++, Go, Rust, and PHP ecosystems.
 
 ## Portability Strategy
@@ -20,3 +23,616 @@ PocketFlow has ports across TypeScript, Java, C++, Go, Rust, and PHP ecosystems.
 You now understand how PocketFlow patterns can transfer across language stacks.
 
 Next: [Chapter 8: Production Usage and Scaling](08-production-usage-and-scaling.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- tutorial slug: **pocketflow-tutorial**
+- chapter focus: **Chapter 7: Multi-Language Ecosystem**
+- system context: **Pocketflow Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Multi-Language Ecosystem`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [PocketFlow Repository](https://github.com/The-Pocket/PocketFlow)
+- [PocketFlow Docs](https://the-pocket.github.io/PocketFlow/)
+- [PocketFlow Cookbook](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook)
+
+### Cross-Tutorial Connection Map
+
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Agno Tutorial](../agno-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Multi-Language Ecosystem`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 7: Multi-Language Ecosystem
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Multi-Language Ecosystem` as an operating subsystem inside **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Multi-Language Ecosystem` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [PocketFlow Repository](https://github.com/The-Pocket/PocketFlow)
+  Why it matters: authoritative reference on `PocketFlow Repository` (github.com).
+- [PocketFlow Docs](https://the-pocket.github.io/PocketFlow/)
+  Why it matters: authoritative reference on `PocketFlow Docs` (the-pocket.github.io).
+- [PocketFlow Cookbook](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook)
+  Why it matters: authoritative reference on `PocketFlow Cookbook` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Streaming, HITL, and Interrupts](06-streaming-hitl-and-interrupts.md)
+- [Next Chapter: Chapter 8: Production Usage and Scaling](08-production-usage-and-scaling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/pocketflow-tutorial/08-production-usage-and-scaling.md b/tutorials/pocketflow-tutorial/08-production-usage-and-scaling.md
index 3a2ab9fb..5544cbb5 100644
--- a/tutorials/pocketflow-tutorial/08-production-usage-and-scaling.md
+++ b/tutorials/pocketflow-tutorial/08-production-usage-and-scaling.md
@@ -7,6 +7,9 @@ parent: PocketFlow Tutorial
 
 # Chapter 8: Production Usage and Scaling
 
+Welcome to **Chapter 8: Production Usage and Scaling**. In this part of **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter outlines how to run PocketFlow systems reliably in production contexts.
 
 ## Operations Checklist
@@ -19,3 +22,615 @@ This chapter outlines how to run PocketFlow systems reliably in production conte
 ## Summary
 
 You now have an operations baseline for production PocketFlow workloads.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- tutorial slug: **pocketflow-tutorial**
+- chapter focus: **Chapter 8: Production Usage and Scaling**
+- system context: **Pocketflow Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Usage and Scaling`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [PocketFlow Repository](https://github.com/The-Pocket/PocketFlow)
+- [PocketFlow Docs](https://the-pocket.github.io/PocketFlow/)
+- [PocketFlow Cookbook](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook)
+
+### Cross-Tutorial Connection Map
+
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Agno Tutorial](../agno-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Usage and Scaling`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 8: Production Usage and Scaling
+
+- tutorial context: **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Usage and Scaling` as an operating subsystem inside **PocketFlow Tutorial: Minimal LLM Framework with Graph-Based Power**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Usage and Scaling` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [PocketFlow Repository](https://github.com/The-Pocket/PocketFlow)
+  Why it matters: authoritative reference on `PocketFlow Repository` (github.com).
+- [PocketFlow Docs](https://the-pocket.github.io/PocketFlow/)
+  Why it matters: authoritative reference on `PocketFlow Docs` (the-pocket.github.io).
+- [PocketFlow Cookbook](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook)
+  Why it matters: authoritative reference on `PocketFlow Cookbook` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Multi-Language Ecosystem](07-multi-language-ecosystem.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/postgresql-query-planner/01-fundamentals.md b/tutorials/postgresql-query-planner/01-fundamentals.md
index 326914b2..8edc6335 100644
--- a/tutorials/postgresql-query-planner/01-fundamentals.md
+++ b/tutorials/postgresql-query-planner/01-fundamentals.md
@@ -7,6 +7,9 @@ nav_order: 1
 
 # Chapter 1: Query Planning Fundamentals
 
+Welcome to **Chapter 1: Query Planning Fundamentals**. In this part of **PostgreSQL Query Planner Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Understand how PostgreSQL transforms SQL into execution plans and learn to read EXPLAIN output effectively.
 
 ## Overview
@@ -387,3 +390,48 @@ Now that you understand the basics, let's dive into how PostgreSQL collects and
 **Ready for Chapter 2?** [Statistics and Cost Estimation](02-statistics.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `SELECT`, `Scan`, `EXPLAIN` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Query Planning Fundamentals` as an operating subsystem inside **PostgreSQL Query Planner Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `WHERE`, `cost`, `rows` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Query Planning Fundamentals` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `SELECT`.
+2. **Input normalization**: shape incoming data so `Scan` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `EXPLAIN`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `SELECT` and `Scan` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Statistics and Cost Estimation](02-statistics.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/postgresql-query-planner/02-statistics.md b/tutorials/postgresql-query-planner/02-statistics.md
index e90e21ac..525b6502 100644
--- a/tutorials/postgresql-query-planner/02-statistics.md
+++ b/tutorials/postgresql-query-planner/02-statistics.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Statistics and Cost Estimation
 
+Welcome to **Chapter 2: Statistics and Cost Estimation**. In this part of **PostgreSQL Query Planner Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deep dive into PostgreSQL statistics, how the planner estimates costs, and the impact of accurate statistics on query performance.
 
 ## Overview
@@ -428,3 +431,49 @@ With a solid understanding of statistics, let's explore how PostgreSQL executes
 **Ready for Chapter 3?** [Scan Operations](03-scan-operations.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `SELECT`, `WHERE`, `attname` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Statistics and Cost Estimation` as an operating subsystem inside **PostgreSQL Query Planner Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `orders`, `rows`, `ANALYZE` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Statistics and Cost Estimation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `SELECT`.
+2. **Input normalization**: shape incoming data so `WHERE` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `attname`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `SELECT` and `WHERE` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Query Planning Fundamentals](01-fundamentals.md)
+- [Next Chapter: Chapter 3: Scan Operations](03-scan-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/postgresql-query-planner/03-scan-operations.md b/tutorials/postgresql-query-planner/03-scan-operations.md
index c3f7f97c..8adde060 100644
--- a/tutorials/postgresql-query-planner/03-scan-operations.md
+++ b/tutorials/postgresql-query-planner/03-scan-operations.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Scan Operations
 
+Welcome to **Chapter 3: Scan Operations**. In this part of **PostgreSQL Query Planner Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Explore sequential scans, index scans, bitmap scans, and understand when PostgreSQL chooses each method.
 
 ## Overview
@@ -404,3 +407,49 @@ Now that you understand scan operations, let's explore how PostgreSQL joins tabl
 **Ready for Chapter 4?** [Join Strategies](04-join-strategies.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `SELECT`, `WHERE`, `customer_id` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Scan Operations` as an operating subsystem inside **PostgreSQL Query Planner Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Index`, `Scan`, `EXPLAIN` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Scan Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `SELECT`.
+2. **Input normalization**: shape incoming data so `WHERE` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `customer_id`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `SELECT` and `WHERE` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Statistics and Cost Estimation](02-statistics.md)
+- [Next Chapter: Chapter 4: Join Strategies](04-join-strategies.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/postgresql-query-planner/04-join-strategies.md b/tutorials/postgresql-query-planner/04-join-strategies.md
index d998e50b..45ce5eb8 100644
--- a/tutorials/postgresql-query-planner/04-join-strategies.md
+++ b/tutorials/postgresql-query-planner/04-join-strategies.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Join Strategies
 
+Welcome to **Chapter 4: Join Strategies**. In this part of **PostgreSQL Query Planner Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master nested loop, hash join, and merge join operations, including when each is optimal.
 
 ## Overview
@@ -462,3 +465,49 @@ Now that you understand join operations, let's dive deep into indexing strategie
 **Ready for Chapter 5?** [Index Deep Dive](05-index-strategies.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `SELECT`, `orders`, `customer_id` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Join Strategies` as an operating subsystem inside **PostgreSQL Query Planner Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `JOIN`, `customers`, `EXPLAIN` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Join Strategies` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `SELECT`.
+2. **Input normalization**: shape incoming data so `orders` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `customer_id`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `SELECT` and `orders` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Scan Operations](03-scan-operations.md)
+- [Next Chapter: Chapter 5: Index Deep Dive](05-index-strategies.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/postgresql-query-planner/05-index-strategies.md b/tutorials/postgresql-query-planner/05-index-strategies.md
index b8b8be35..ee7039fe 100644
--- a/tutorials/postgresql-query-planner/05-index-strategies.md
+++ b/tutorials/postgresql-query-planner/05-index-strategies.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Index Deep Dive
 
+Welcome to **Chapter 5: Index Deep Dive**. In this part of **PostgreSQL Query Planner Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Advanced indexing strategies including B-tree internals, partial indexes, expression indexes, and covering indexes.
 
 ## Overview
@@ -411,3 +414,49 @@ Now that you understand indexing, let's explore advanced query optimization tech
 **Ready for Chapter 6?** [Advanced Optimization](06-advanced-optimization.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `WHERE`, `CREATE`, `INDEX` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Index Deep Dive` as an operating subsystem inside **PostgreSQL Query Planner Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `SELECT`, `orders`, `EXPLAIN` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Index Deep Dive` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `WHERE`.
+2. **Input normalization**: shape incoming data so `CREATE` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `INDEX`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `WHERE` and `CREATE` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Join Strategies](04-join-strategies.md)
+- [Next Chapter: Chapter 6: Advanced Optimization](06-advanced-optimization.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/postgresql-query-planner/06-advanced-optimization.md b/tutorials/postgresql-query-planner/06-advanced-optimization.md
index 55b9db0f..16c8e37e 100644
--- a/tutorials/postgresql-query-planner/06-advanced-optimization.md
+++ b/tutorials/postgresql-query-planner/06-advanced-optimization.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Advanced Optimization
 
+Welcome to **Chapter 6: Advanced Optimization**. In this part of **PostgreSQL Query Planner Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > CTEs, window functions, subquery optimization, and parallel query execution.
 
 ## Overview
@@ -421,3 +424,49 @@ With advanced optimization knowledge, let's move to performance tuning configura
 **Ready for Chapter 7?** [Performance Tuning](07-performance-tuning.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `SELECT`, `customer_id`, `orders` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Advanced Optimization` as an operating subsystem inside **PostgreSQL Query Planner Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `WHERE`, `total`, `EXPLAIN` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Advanced Optimization` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `SELECT`.
+2. **Input normalization**: shape incoming data so `customer_id` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `orders`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `SELECT` and `customer_id` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Index Deep Dive](05-index-strategies.md)
+- [Next Chapter: Chapter 7: Performance Tuning](07-performance-tuning.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/postgresql-query-planner/07-performance-tuning.md b/tutorials/postgresql-query-planner/07-performance-tuning.md
index e9112f05..5f5d5f77 100644
--- a/tutorials/postgresql-query-planner/07-performance-tuning.md
+++ b/tutorials/postgresql-query-planner/07-performance-tuning.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Performance Tuning
 
+Welcome to **Chapter 7: Performance Tuning**. In this part of **PostgreSQL Query Planner Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Configuration parameters, memory settings, and systematic approaches to query optimization.
 
 ## Overview
@@ -433,3 +436,49 @@ With configuration knowledge in hand, let's explore real-world patterns and anti
 **Ready for Chapter 8?** [Real-World Patterns](08-real-world-patterns.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `SELECT`, `WHERE`, `SHOW` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Performance Tuning` as an operating subsystem inside **PostgreSQL Query Planner Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `queries`, `round`, `work_mem` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Performance Tuning` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `SELECT`.
+2. **Input normalization**: shape incoming data so `WHERE` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `SHOW`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `SELECT` and `WHERE` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Advanced Optimization](06-advanced-optimization.md)
+- [Next Chapter: Chapter 8: Real-World Patterns](08-real-world-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/postgresql-query-planner/08-real-world-patterns.md b/tutorials/postgresql-query-planner/08-real-world-patterns.md
index aa5c62ce..753ad397 100644
--- a/tutorials/postgresql-query-planner/08-real-world-patterns.md
+++ b/tutorials/postgresql-query-planner/08-real-world-patterns.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Real-World Patterns
 
+Welcome to **Chapter 8: Real-World Patterns**. In this part of **PostgreSQL Query Planner Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Common anti-patterns, production debugging techniques, and optimization case studies.
 
 ## Overview
@@ -463,3 +466,48 @@ Congratulations! You've completed the PostgreSQL Query Planner tutorial. You now
 ---
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `SELECT`, `WHERE`, `orders` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Real-World Patterns` as an operating subsystem inside **PostgreSQL Query Planner Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `customer_id`, `created_at`, `Solution` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Real-World Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `SELECT`.
+2. **Input normalization**: shape incoming data so `WHERE` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `orders`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `SELECT` and `WHERE` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Performance Tuning](07-performance-tuning.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/posthog-tutorial/01-getting-started.md b/tutorials/posthog-tutorial/01-getting-started.md
index e5b14fcc..4b594ca1 100644
--- a/tutorials/posthog-tutorial/01-getting-started.md
+++ b/tutorials/posthog-tutorial/01-getting-started.md
@@ -391,3 +391,48 @@ Now that you have PostHog collecting data, let's explore event tracking patterns
 4. Explore the PostHog dashboard and available insights
 
 *What user behavior are you most curious about tracking in your application?* 📊
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `posthog`, `PostHog`, `capture` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with PostHog` as an operating subsystem inside **PostHog Tutorial: Open Source Product Analytics Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `your`, `Track`, `https` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with PostHog` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `posthog`.
+2. **Input normalization**: shape incoming data so `PostHog` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `capture`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/PostHog/posthog)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `posthog` and `PostHog` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Event Tracking & Properties](02-event-tracking.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/posthog-tutorial/02-event-tracking.md b/tutorials/posthog-tutorial/02-event-tracking.md
index 807904f0..8e683f2c 100644
--- a/tutorials/posthog-tutorial/02-event-tracking.md
+++ b/tutorials/posthog-tutorial/02-event-tracking.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Event Tracking & Properties
 
+Welcome to **Chapter 2: Event Tracking & Properties**. In this part of **PostHog Tutorial: Open Source Product Analytics Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 1](01-getting-started.md), you set up PostHog, sent your first event, and identified a user. Now it is time to build a production-quality event tracking layer. Good event design is the foundation of every insight, funnel, and experiment you will create later. A sloppy taxonomy leads to broken dashboards and misleading metrics; a clean one compounds value over time.
 
 This chapter covers naming conventions, property design, autocapture, group analytics, and server-side tracking so you can instrument any surface -- web, mobile, or backend -- with confidence.
@@ -612,3 +615,49 @@ With a solid event tracking foundation in place, you are ready to analyze user b
 ---
 
 *Built with insights from the [PostHog](https://github.com/PostHog/posthog) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `posthog`, `event`, `properties` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Event Tracking & Properties` as an operating subsystem inside **PostHog Tutorial: Open Source Product Analytics Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `capture`, `PostHog`, `plan` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Event Tracking & Properties` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `posthog`.
+2. **Input normalization**: shape incoming data so `event` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `properties`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/PostHog/posthog)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `posthog` and `event` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with PostHog](01-getting-started.md)
+- [Next Chapter: Chapter 3: User Analytics & Funnels](03-user-analytics.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/posthog-tutorial/03-user-analytics.md b/tutorials/posthog-tutorial/03-user-analytics.md
index a80dacdb..b0d6a590 100644
--- a/tutorials/posthog-tutorial/03-user-analytics.md
+++ b/tutorials/posthog-tutorial/03-user-analytics.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: User Analytics & Funnels
 
+Welcome to **Chapter 3: User Analytics & Funnels**. In this part of **PostHog Tutorial: Open Source Product Analytics Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 2](02-event-tracking.md), you built a solid event tracking layer with clean naming conventions, rich properties, and identity resolution. Raw events are the ingredients; analytics is the recipe. This chapter shows you how to turn those ingredients into funnels, retention curves, user paths, and trend analyses that drive real product decisions.
 
 By the end of this chapter you will be able to answer questions like "Where do users drop off during onboarding?", "How many users come back after week one?", and "Which acquisition channel produces the most paying customers?"
@@ -528,3 +531,49 @@ You now know how to analyze user behavior quantitatively. But numbers only tell
 ---
 
 *Built with insights from the [PostHog](https://github.com/PostHog/posthog) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `classDef`, `fill`, `stroke` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: User Analytics & Funnels` as an operating subsystem inside **PostHog Tutorial: Open Source Product Analytics Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Week`, `json`, `order` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: User Analytics & Funnels` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `classDef`.
+2. **Input normalization**: shape incoming data so `fill` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `stroke`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/PostHog/posthog)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `classDef` and `fill` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Event Tracking & Properties](02-event-tracking.md)
+- [Next Chapter: Chapter 4: Session Recordings](04-session-recordings.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/posthog-tutorial/04-session-recordings.md b/tutorials/posthog-tutorial/04-session-recordings.md
index e28db70c..6f9ca6d9 100644
--- a/tutorials/posthog-tutorial/04-session-recordings.md
+++ b/tutorials/posthog-tutorial/04-session-recordings.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Session Recordings
 
+Welcome to **Chapter 4: Session Recordings**. In this part of **PostHog Tutorial: Open Source Product Analytics Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 3](03-user-analytics.md), you built funnels, retention tables, and trend analyses to understand *what* users do. But quantitative data only tells half the story. When a funnel shows a 58% drop-off at the checkout step, the numbers alone cannot tell you *why* users abandoned. Session recordings bridge that gap.
 
 PostHog's session recording feature captures a DOM-based replay of every user interaction -- clicks, scrolls, page navigations, console logs, and network requests -- so you can watch exactly what happened during a session. This chapter covers how to enable recordings, filter them effectively, connect them to your analytics insights, and handle the privacy considerations that come with watching real user behavior.
@@ -532,3 +535,49 @@ Now that you can observe user behavior qualitatively, you are ready to act on yo
 ---
 
 *Built with insights from the [PostHog](https://github.com/PostHog/posthog) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `posthog`, `text`, `user` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Session Recordings` as an operating subsystem inside **PostHog Tutorial: Open Source Product Analytics Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `recording`, `capture`, `Filters` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Session Recordings` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `posthog`.
+2. **Input normalization**: shape incoming data so `text` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `user`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/PostHog/posthog)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `posthog` and `text` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: User Analytics & Funnels](03-user-analytics.md)
+- [Next Chapter: Chapter 5: Feature Flags & Experiments](05-feature-flags.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/posthog-tutorial/05-feature-flags.md b/tutorials/posthog-tutorial/05-feature-flags.md
index c801a3d2..f0e5c859 100644
--- a/tutorials/posthog-tutorial/05-feature-flags.md
+++ b/tutorials/posthog-tutorial/05-feature-flags.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Feature Flags & Experiments
 
+Welcome to **Chapter 5: Feature Flags & Experiments**. In this part of **PostHog Tutorial: Open Source Product Analytics Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 4](04-session-recordings.md), you learned how to watch real user sessions to understand friction points. Now it is time to fix those problems -- safely. Feature flags let you ship code to production without exposing it to every user, and experiments let you measure whether your changes actually improve the metrics that matter.
 
 This chapter covers the full lifecycle of feature flags and A/B tests in PostHog: creating flags, evaluating them on the client and server, targeting specific cohorts, running statistically rigorous experiments, and managing the cleanup that keeps your codebase from drowning in flag conditionals.
@@ -672,3 +675,49 @@ With feature flags and experiments in your toolkit, you need a way to present al
 ---
 
 *Built with insights from the [PostHog](https://github.com/PostHog/posthog) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `posthog`, `variant`, `flag` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Feature Flags & Experiments` as an operating subsystem inside **PostHog Tutorial: Open Source Product Analytics Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `checkout`, `flow`, `classDef` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Feature Flags & Experiments` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `posthog`.
+2. **Input normalization**: shape incoming data so `variant` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `flag`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/PostHog/posthog)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `posthog` and `variant` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Session Recordings](04-session-recordings.md)
+- [Next Chapter: Chapter 6: Dashboards & Insights](06-dashboards-insights.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/posthog-tutorial/06-dashboards-insights.md b/tutorials/posthog-tutorial/06-dashboards-insights.md
index b3a490b0..eb98abcc 100644
--- a/tutorials/posthog-tutorial/06-dashboards-insights.md
+++ b/tutorials/posthog-tutorial/06-dashboards-insights.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Dashboards & Insights
 
+Welcome to **Chapter 6: Dashboards & Insights**. In this part of **PostHog Tutorial: Open Source Product Analytics Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In the previous chapters you built the entire analytics pipeline: event tracking (Chapter 2), user analytics (Chapter 3), session recordings (Chapter 4), and feature flags (Chapter 5). Each of those tools produces individual insights. Dashboards are where you bring them together into a coherent story that your team, your managers, and your stakeholders can understand at a glance.
 
 A well-designed dashboard answers a specific question for a specific audience. A poorly designed one is a wall of charts that nobody reads. This chapter teaches you how to design dashboards that people actually use, how to create and configure insights in PostHog, and how to set up automated reports and alerts so the data comes to you.
@@ -646,3 +649,49 @@ Your dashboards are now telling the story of your product. But sometimes the sta
 ---
 
 *Built with insights from the [PostHog](https://github.com/PostHog/posthog) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `json`, `insight`, `dashboard` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Dashboards & Insights` as an operating subsystem inside **PostHog Tutorial: Open Source Product Analytics Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `headers`, `name`, `classDef` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Dashboards & Insights` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `json`.
+2. **Input normalization**: shape incoming data so `insight` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `dashboard`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/PostHog/posthog)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `json` and `insight` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Feature Flags & Experiments](05-feature-flags.md)
+- [Next Chapter: Chapter 7: Advanced Analytics](07-advanced-analytics.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/posthog-tutorial/07-advanced-analytics.md b/tutorials/posthog-tutorial/07-advanced-analytics.md
index 6876e004..27a3663f 100644
--- a/tutorials/posthog-tutorial/07-advanced-analytics.md
+++ b/tutorials/posthog-tutorial/07-advanced-analytics.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Advanced Analytics
 
+Welcome to **Chapter 7: Advanced Analytics**. In this part of **PostHog Tutorial: Open Source Product Analytics Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 6](06-dashboards-insights.md), you built dashboards that communicate metrics to stakeholders. Those dashboards use PostHog's built-in insight types -- trends, funnels, retention -- which cover the majority of product analytics questions. But some questions require more power: custom SQL queries, advanced cohort logic, computed metrics, and data warehouse integrations.
 
 This chapter covers PostHog's advanced analytics capabilities: HogQL (PostHog's SQL dialect), programmatic cohort management, data pipelines for warehouse synchronization, and techniques for building metrics like LTV, churn prediction, and attribution modeling that go beyond what point-and-click insights can deliver.
@@ -764,3 +767,49 @@ You now have the analytical tools to answer virtually any product question. The
 ---
 
 *Built with insights from the [PostHog](https://github.com/PostHog/posthog) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `events`, `event`, `distinct_id` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Advanced Analytics` as an operating subsystem inside **PostHog Tutorial: Open Source Product Analytics Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `timestamp`, `SELECT`, `count` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Advanced Analytics` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `events`.
+2. **Input normalization**: shape incoming data so `event` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `distinct_id`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/PostHog/posthog)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `events` and `event` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Dashboards & Insights](06-dashboards-insights.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/posthog-tutorial/08-production-deployment.md b/tutorials/posthog-tutorial/08-production-deployment.md
index f34f9661..756a5374 100644
--- a/tutorials/posthog-tutorial/08-production-deployment.md
+++ b/tutorials/posthog-tutorial/08-production-deployment.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **PostHog Tutorial: Open Source Product Analytics Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Throughout this tutorial you built an analytics stack from the ground up: event tracking (Chapter 2), user analytics (Chapter 3), session recordings (Chapter 4), feature flags (Chapter 5), dashboards (Chapter 6), and advanced analytics (Chapter 7). All of that work is only as valuable as the infrastructure running it. A misconfigured deployment loses events, a breach exposes user data, and an unmonitored system fails silently.
 
 This final chapter covers everything you need to run PostHog in production with confidence: choosing between cloud and self-hosted, hardening your ingestion pipeline, monitoring system health, managing costs, ensuring compliance, and planning for scale.
@@ -953,3 +956,48 @@ Congratulations -- you have completed the PostHog tutorial. You now have the kno
 4. Configure backup and test a restore procedure
 
 *Built with insights from the [PostHog](https://github.com/PostHog/posthog) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `posthog`, `checks`, `PostHog` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **PostHog Tutorial: Open Source Product Analytics Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `classDef`, `fill`, `stroke` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `posthog`.
+2. **Input normalization**: shape incoming data so `checks` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `PostHog`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/PostHog/posthog)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `posthog` and `checks` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Advanced Analytics](07-advanced-analytics.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/pydantic-ai-tutorial/01-getting-started.md b/tutorials/pydantic-ai-tutorial/01-getting-started.md
index 54f48c6a..c029a0ae 100644
--- a/tutorials/pydantic-ai-tutorial/01-getting-started.md
+++ b/tutorials/pydantic-ai-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: Pydantic AI Tutorial
 
 # Chapter 1: Getting Started with Pydantic AI
 
+Welcome to **Chapter 1: Getting Started with Pydantic AI**. In this part of **Pydantic AI Tutorial: Type-Safe AI Agent Development**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Build your first type-safe AI agent with guaranteed structured outputs using Pydantic AI.
 
 ## Installation
@@ -552,4 +555,49 @@ Now that you understand the basics of Pydantic AI, let's explore:
 - [ ] Implement basic error handling
 - [ ] Test async operations and streaming
 
-You're now ready to build type-safe, production-ready AI agents! 🚀
\ No newline at end of file
+You're now ready to build type-safe, production-ready AI agents! 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `print`, `Agent`, `result` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Pydantic AI` as an operating subsystem inside **Pydantic AI Tutorial: Type-Safe AI Agent Development**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `agent`, `self`, `openai` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Pydantic AI` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `print`.
+2. **Input normalization**: shape incoming data so `Agent` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `result`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/pydantic/pydantic-ai)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `print` and `Agent` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Advanced Model Configuration & Provider Setup](02-model-configuration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/pydantic-ai-tutorial/02-model-configuration.md b/tutorials/pydantic-ai-tutorial/02-model-configuration.md
index b360d4bc..7d7f8d33 100644
--- a/tutorials/pydantic-ai-tutorial/02-model-configuration.md
+++ b/tutorials/pydantic-ai-tutorial/02-model-configuration.md
@@ -8,6 +8,9 @@ parent: Pydantic AI Tutorial
 
 # Chapter 2: Advanced Model Configuration & Provider Setup
 
+Welcome to **Chapter 2: Advanced Model Configuration & Provider Setup**. In this part of **Pydantic AI Tutorial: Type-Safe AI Agent Development**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master multi-provider setups, fallback strategies, and advanced model configuration for robust AI agent systems.
 
 ## Multi-Provider Setup
@@ -924,4 +927,50 @@ best_provider = health_monitor.get_best_provider({'speed': 'fast'})
 print(f"Best provider for speed: {best_provider}")
 ```
 
-This comprehensive model configuration chapter covers advanced provider setup, fallback strategies, intelligent model selection, and health monitoring for robust, production-ready AI agent systems. 🚀
\ No newline at end of file
+This comprehensive model configuration chapter covers advanced provider setup, fallback strategies, intelligent model selection, and health monitoring for robust, production-ready AI agent systems. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `print`, `providers` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Advanced Model Configuration & Provider Setup` as an operating subsystem inside **Pydantic AI Tutorial: Type-Safe AI Agent Development**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `TaskType`, `prompt`, `Agent` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Advanced Model Configuration & Provider Setup` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `print` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `providers`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/pydantic/pydantic-ai)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `print` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Pydantic AI](01-getting-started.md)
+- [Next Chapter: Chapter 3: Structured Outputs & Pydantic Models](03-structured-outputs.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/pydantic-ai-tutorial/03-structured-outputs.md b/tutorials/pydantic-ai-tutorial/03-structured-outputs.md
index 8d0df34b..fdec043f 100644
--- a/tutorials/pydantic-ai-tutorial/03-structured-outputs.md
+++ b/tutorials/pydantic-ai-tutorial/03-structured-outputs.md
@@ -8,6 +8,9 @@ parent: Pydantic AI Tutorial
 
 # Chapter 3: Structured Outputs & Pydantic Models
 
+Welcome to **Chapter 3: Structured Outputs & Pydantic Models**. In this part of **Pydantic AI Tutorial: Type-Safe AI Agent Development**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master guaranteed structured data generation with complex Pydantic models, validation, and type safety.
 
 ## Basic Structured Output
@@ -702,4 +705,50 @@ if schema_response.status_code == 200:
     print(f"Person schema has {len(schemas['person']['properties'])} properties")
 ```
 
-This comprehensive structured outputs chapter demonstrates how to generate guaranteed valid data structures using Pydantic models, complex validation rules, and seamless API integration. The type safety ensures that generated data always conforms to your specifications. 🚀
\ No newline at end of file
+This comprehensive structured outputs chapter demonstrates how to generate guaranteed valid data structures using Pydantic models, complex validation rules, and seamless API integration. The type safety ensures that generated data always conforms to your specifications. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `print`, `Field`, `result` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Structured Outputs & Pydantic Models` as an operating subsystem inside **Pydantic AI Tutorial: Type-Safe AI Agent Development**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `BaseModel`, `self`, `item` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Structured Outputs & Pydantic Models` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `print`.
+2. **Input normalization**: shape incoming data so `Field` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `result`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/pydantic/pydantic-ai)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `print` and `Field` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Advanced Model Configuration & Provider Setup](02-model-configuration.md)
+- [Next Chapter: Chapter 4: Dependencies, Tools & External Integrations](04-dependencies-tools.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/pydantic-ai-tutorial/04-dependencies-tools.md b/tutorials/pydantic-ai-tutorial/04-dependencies-tools.md
index 2116606d..91534867 100644
--- a/tutorials/pydantic-ai-tutorial/04-dependencies-tools.md
+++ b/tutorials/pydantic-ai-tutorial/04-dependencies-tools.md
@@ -8,6 +8,9 @@ parent: Pydantic AI Tutorial
 
 # Chapter 4: Dependencies, Tools & External Integrations
 
+Welcome to **Chapter 4: Dependencies, Tools & External Integrations**. In this part of **Pydantic AI Tutorial: Type-Safe AI Agent Development**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Extend Pydantic AI agents with custom tools, dependencies, and external service integrations for comprehensive task completion.
 
 ## Tool System Architecture
@@ -982,4 +985,50 @@ for result in parallel_results['successful']:
     print(f"  {task['tool']}: {output[:100]}...")
 ```
 
-This comprehensive dependencies and tools chapter demonstrates how to extend Pydantic AI agents with powerful external integrations, tool chaining, and orchestration patterns for complex task completion. 🚀
\ No newline at end of file
+This comprehensive dependencies and tools chapter demonstrates how to extend Pydantic AI agents with powerful external integrations, tool chaining, and orchestration patterns for complex task completion. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `tool`, `result` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Dependencies, Tools & External Integrations` as an operating subsystem inside **Pydantic AI Tutorial: Type-Safe AI Agent Development**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Dict`, `query`, `print` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Dependencies, Tools & External Integrations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `tool` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `result`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/pydantic/pydantic-ai)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `tool` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Structured Outputs & Pydantic Models](03-structured-outputs.md)
+- [Next Chapter: Chapter 5: Streaming Responses & Async Operations](05-streaming-async.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/pydantic-ai-tutorial/05-streaming-async.md b/tutorials/pydantic-ai-tutorial/05-streaming-async.md
index 9d59aeb0..85be4ea6 100644
--- a/tutorials/pydantic-ai-tutorial/05-streaming-async.md
+++ b/tutorials/pydantic-ai-tutorial/05-streaming-async.md
@@ -8,6 +8,9 @@ parent: Pydantic AI Tutorial
 
 # Chapter 5: Streaming Responses & Async Operations
 
+Welcome to **Chapter 5: Streaming Responses & Async Operations**. In this part of **Pydantic AI Tutorial: Type-Safe AI Agent Development**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master real-time streaming, asynchronous processing, and concurrent operations for high-performance Pydantic AI applications.
 
 ## Basic Streaming
@@ -757,4 +760,50 @@ async def batch_streaming_demo():
 asyncio.run(batch_streaming_demo())
 ```
 
-This comprehensive streaming and async chapter demonstrates advanced patterns for real-time response generation, concurrent processing, and high-performance agent operations. 🚀
\ No newline at end of file
+This comprehensive streaming and async chapter demonstrates advanced patterns for real-time response generation, concurrent processing, and high-performance agent operations. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `print`, `result`, `self` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Streaming Responses & Async Operations` as an operating subsystem inside **Pydantic AI Tutorial: Type-Safe AI Agent Development**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `agent`, `time`, `Agent` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Streaming Responses & Async Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `print`.
+2. **Input normalization**: shape incoming data so `result` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `self`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/pydantic/pydantic-ai)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `print` and `result` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Dependencies, Tools & External Integrations](04-dependencies-tools.md)
+- [Next Chapter: Chapter 6: Error Handling, Retry Mechanisms & Recovery](06-error-handling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/pydantic-ai-tutorial/06-error-handling.md b/tutorials/pydantic-ai-tutorial/06-error-handling.md
index 53b74ba7..bd650fb5 100644
--- a/tutorials/pydantic-ai-tutorial/06-error-handling.md
+++ b/tutorials/pydantic-ai-tutorial/06-error-handling.md
@@ -8,6 +8,9 @@ parent: Pydantic AI Tutorial
 
 # Chapter 6: Error Handling, Retry Mechanisms & Recovery
 
+Welcome to **Chapter 6: Error Handling, Retry Mechanisms & Recovery**. In this part of **Pydantic AI Tutorial: Type-Safe AI Agent Development**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Build robust Pydantic AI applications with comprehensive error handling, retry strategies, and graceful failure recovery.
 
 ## Basic Error Handling
@@ -1061,4 +1064,50 @@ async def test_recovery_workflow():
 asyncio.run(test_recovery_workflow())
 ```
 
-This comprehensive error handling chapter demonstrates robust error management, retry strategies, circuit breakers, graceful degradation, monitoring, and automated recovery procedures for production-ready Pydantic AI applications. 🚀
\ No newline at end of file
+This comprehensive error handling chapter demonstrates robust error management, retry strategies, circuit breakers, graceful degradation, monitoring, and automated recovery procedures for production-ready Pydantic AI applications. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `print`, `agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Error Handling, Retry Mechanisms & Recovery` as an operating subsystem inside **Pydantic AI Tutorial: Type-Safe AI Agent Development**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `error`, `prompt`, `time` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Error Handling, Retry Mechanisms & Recovery` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `print` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/pydantic/pydantic-ai)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `print` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Streaming Responses & Async Operations](05-streaming-async.md)
+- [Next Chapter: Chapter 7: Advanced Patterns & Multi-Step Workflows](07-advanced-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/pydantic-ai-tutorial/07-advanced-patterns.md b/tutorials/pydantic-ai-tutorial/07-advanced-patterns.md
index ce36d0d0..f374df56 100644
--- a/tutorials/pydantic-ai-tutorial/07-advanced-patterns.md
+++ b/tutorials/pydantic-ai-tutorial/07-advanced-patterns.md
@@ -8,6 +8,9 @@ parent: Pydantic AI Tutorial
 
 # Chapter 7: Advanced Patterns & Multi-Step Workflows
 
+Welcome to **Chapter 7: Advanced Patterns & Multi-Step Workflows**. In this part of **Pydantic AI Tutorial: Type-Safe AI Agent Development**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master complex agent workflows, dynamic reasoning patterns, and sophisticated interaction paradigms for advanced AI applications.
 
 ## Dynamic Agent Composition
@@ -975,4 +978,50 @@ async def test_self_improvement():
 asyncio.run(test_self_improvement())
 ```
 
-This advanced patterns chapter demonstrates sophisticated agent architectures including dynamic composition, chain-of-thought reasoning, multi-agent collaboration, and self-improving systems that learn from feedback and experience. 🚀
\ No newline at end of file
+This advanced patterns chapter demonstrates sophisticated agent architectures including dynamic composition, chain-of-thought reasoning, multi-agent collaboration, and self-improving systems that learn from feedback and experience. 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `print`, `agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Advanced Patterns & Multi-Step Workflows` as an operating subsystem inside **Pydantic AI Tutorial: Type-Safe AI Agent Development**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `result`, `Dict`, `task` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Advanced Patterns & Multi-Step Workflows` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `print` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/pydantic/pydantic-ai)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `print` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Error Handling, Retry Mechanisms & Recovery](06-error-handling.md)
+- [Next Chapter: Chapter 8: Production Deployment & Scaling Pydantic AI Systems](08-production.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/pydantic-ai-tutorial/08-production.md b/tutorials/pydantic-ai-tutorial/08-production.md
index 8d428b38..aa3fd50a 100644
--- a/tutorials/pydantic-ai-tutorial/08-production.md
+++ b/tutorials/pydantic-ai-tutorial/08-production.md
@@ -8,6 +8,9 @@ parent: Pydantic AI Tutorial
 
 # Chapter 8: Production Deployment & Scaling Pydantic AI Systems
 
+Welcome to **Chapter 8: Production Deployment & Scaling Pydantic AI Systems**. In this part of **Pydantic AI Tutorial: Type-Safe AI Agent Development**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy type-safe AI agent systems at enterprise scale with high availability, monitoring, and production best practices.
 
 ## Production Architecture
@@ -1547,4 +1550,49 @@ curl https://agents.company.com/metrics
 python production_benchmarks.py
 ```
 
-This completes the comprehensive Pydantic AI production deployment guide! 🎊
\ No newline at end of file
+This completes the comprehensive Pydantic AI production deployment guide! 🎊
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `user_id`, `model` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment & Scaling Pydantic AI Systems` as an operating subsystem inside **Pydantic AI Tutorial: Type-Safe AI Agent Development**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `agent`, `time`, `summary` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment & Scaling Pydantic AI Systems` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `user_id` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/pydantic/pydantic-ai)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `user_id` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Advanced Patterns & Multi-Step Workflows](07-advanced-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/quivr-tutorial/01-getting-started.md b/tutorials/quivr-tutorial/01-getting-started.md
index b6b56dd0..dac889d7 100644
--- a/tutorials/quivr-tutorial/01-getting-started.md
+++ b/tutorials/quivr-tutorial/01-getting-started.md
@@ -398,3 +398,50 @@ Now that you have Quivr running and have uploaded your first documents, let's ex
 4. Monitor the performance and accuracy of responses
 
 *What's the most interesting document-based question you could ask an AI system?* 📄
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `client`, `print`, `your` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Quivr` as an operating subsystem inside **Quivr Tutorial: Open-Source RAG Framework for Document Ingestion**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `document`, `quivr`, `knowledge_base_id` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Quivr` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `client`.
+2. **Input normalization**: shape incoming data so `print` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `your`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/QuivrHQ/quivr)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `client` and `print` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Document Processing](02-document-processing.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/quivr-tutorial/02-document-processing.md b/tutorials/quivr-tutorial/02-document-processing.md
index b19431a3..672ae1c0 100644
--- a/tutorials/quivr-tutorial/02-document-processing.md
+++ b/tutorials/quivr-tutorial/02-document-processing.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Document Processing
 
+Welcome to **Chapter 2: Document Processing**. In this part of **Quivr Tutorial: Open-Source RAG Framework for Document Ingestion**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 1](01-getting-started.md), you installed Quivr and uploaded your first document. But what actually happens once a file lands in the system? This chapter dives deep into the document processing pipeline -- the engine that transforms raw PDFs, HTML pages, and plain-text files into clean, structured chunks ready for embedding.
 
 Understanding this pipeline is critical because the quality of your RAG responses depends directly on the quality of your ingested text. Garbage in, garbage out. By the end of this chapter you will know how to extract text from every supported format, clean and normalize it, split it into semantically meaningful chunks, and troubleshoot the most common ingestion problems.
@@ -626,3 +629,51 @@ Your documents are now extracted, cleaned, and chunked. In [Chapter 3: Vector Em
 ---
 
 *Built with insights from the [Quivr](https://github.com/QuivrHQ/quivr) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `print`, `chunks`, `processing` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Document Processing` as an operating subsystem inside **Quivr Tutorial: Open-Source RAG Framework for Document Ingestion**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `quivr`, `text`, `chunk` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Document Processing` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `print`.
+2. **Input normalization**: shape incoming data so `chunks` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `processing`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/QuivrHQ/quivr)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `print` and `chunks` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Quivr](01-getting-started.md)
+- [Next Chapter: Chapter 3: Vector Embeddings](03-vector-embeddings.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/quivr-tutorial/03-vector-embeddings.md b/tutorials/quivr-tutorial/03-vector-embeddings.md
index f0ef16a5..4c9761bb 100644
--- a/tutorials/quivr-tutorial/03-vector-embeddings.md
+++ b/tutorials/quivr-tutorial/03-vector-embeddings.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Vector Embeddings
 
+Welcome to **Chapter 3: Vector Embeddings**. In this part of **Quivr Tutorial: Open-Source RAG Framework for Document Ingestion**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 2](02-document-processing.md), you transformed raw documents into clean, well-structured chunks. Now those chunks need to become searchable. This is where vector embeddings come in -- the mathematical representations that let Quivr find semantically similar content even when the exact words differ.
 
 Embeddings are the bridge between human language and machine understanding. When a user asks "What is the company's revenue growth strategy?", the system needs to find chunks about "fiscal expansion plans" and "year-over-year sales targets" even though none of those words appear in the query. This chapter covers everything you need to know about choosing, generating, storing, and optimizing vector embeddings in Quivr.
@@ -616,3 +619,51 @@ Your chunks are now embedded and stored in a vector database. In [Chapter 4: Que
 ---
 
 *Built with insights from the [Quivr](https://github.com/QuivrHQ/quivr) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `print`, `vectors`, `quivr` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Vector Embeddings` as an operating subsystem inside **Quivr Tutorial: Open-Source RAG Framework for Document Ingestion**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `chunks`, `embedder`, `chunk` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Vector Embeddings` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `print`.
+2. **Input normalization**: shape incoming data so `vectors` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `quivr`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/QuivrHQ/quivr)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `print` and `vectors` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Document Processing](02-document-processing.md)
+- [Next Chapter: Chapter 4: Query Processing](04-query-processing.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/quivr-tutorial/04-query-processing.md b/tutorials/quivr-tutorial/04-query-processing.md
index 9ea8e810..a823fb29 100644
--- a/tutorials/quivr-tutorial/04-query-processing.md
+++ b/tutorials/quivr-tutorial/04-query-processing.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Query Processing
 
+Welcome to **Chapter 4: Query Processing**. In this part of **Quivr Tutorial: Open-Source RAG Framework for Document Ingestion**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 3](03-vector-embeddings.md), you embedded your document chunks and stored them in a vector database. Now it is time to close the loop: take a user's question, retrieve the most relevant chunks, and generate an accurate, cited answer. This is where RAG delivers its value.
 
 Query processing is more than just running a similarity search. A well-designed query pipeline normalizes the user's input, expands it with synonyms or rephrased variants, retrieves candidate chunks, reranks them for precision, assembles a prompt with the right amount of context, and finally generates a response with proper citations. This chapter walks through each stage in detail.
@@ -600,3 +603,51 @@ You now have a complete retrieval and generation pipeline. In [Chapter 5: Knowle
 ---
 
 *Built with insights from the [Quivr](https://github.com/QuivrHQ/quivr) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `print`, `query`, `results` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Query Processing` as an operating subsystem inside **Quivr Tutorial: Open-Source RAG Framework for Document Ingestion**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `quivr`, `Quivr`, `result` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Query Processing` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `print`.
+2. **Input normalization**: shape incoming data so `query` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `results`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/QuivrHQ/quivr)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `print` and `query` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Vector Embeddings](03-vector-embeddings.md)
+- [Next Chapter: Chapter 5: Knowledge Bases](05-knowledge-bases.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/quivr-tutorial/05-knowledge-bases.md b/tutorials/quivr-tutorial/05-knowledge-bases.md
index 216e0f3c..49a70ee3 100644
--- a/tutorials/quivr-tutorial/05-knowledge-bases.md
+++ b/tutorials/quivr-tutorial/05-knowledge-bases.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Knowledge Bases
 
+Welcome to **Chapter 5: Knowledge Bases**. In this part of **Quivr Tutorial: Open-Source RAG Framework for Document Ingestion**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 4](04-query-processing.md), you built a complete query processing pipeline that retrieves context and generates answers. But as your document collection grows beyond a handful of files, you need structure. Knowledge bases are the organizational layer that turns a pile of documents into a managed, searchable, access-controlled library.
 
 A knowledge base in Quivr is more than a folder. It is a logical container with its own metadata schema, access policies, embedding configuration, and lifecycle rules. This chapter covers how to create, populate, manage, and govern knowledge bases for teams, projects, and multi-tenant environments.
@@ -585,3 +588,51 @@ Your knowledge bases are organized and governed. In [Chapter 6: Integration APIs
 ---
 
 *Built with insights from the [Quivr](https://github.com/QuivrHQ/quivr) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `print`, `knowledge`, `quivr` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Knowledge Bases` as an operating subsystem inside **Quivr Tutorial: Open-Source RAG Framework for Document Ingestion**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `result`, `classDef`, `fill` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Knowledge Bases` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `print`.
+2. **Input normalization**: shape incoming data so `knowledge` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `quivr`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/QuivrHQ/quivr)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `print` and `knowledge` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Query Processing](04-query-processing.md)
+- [Next Chapter: Chapter 6: Integration APIs](06-integration-apis.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/quivr-tutorial/06-integration-apis.md b/tutorials/quivr-tutorial/06-integration-apis.md
index 8f0ed56c..3c1979f3 100644
--- a/tutorials/quivr-tutorial/06-integration-apis.md
+++ b/tutorials/quivr-tutorial/06-integration-apis.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Integration APIs
 
+Welcome to **Chapter 6: Integration APIs**. In this part of **Quivr Tutorial: Open-Source RAG Framework for Document Ingestion**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 5](05-knowledge-bases.md), you organized your documents into governed knowledge bases. Now it is time to make that knowledge accessible to the rest of your organization. Whether you are building a Slack bot, embedding a search widget in your internal portal, or feeding answers into a CI/CD pipeline, Quivr's APIs are how you connect.
 
 This chapter covers the full spectrum of integration patterns: REST API endpoints for document ingestion and querying, streaming responses via Server-Sent Events, webhook notifications, the Python SDK for programmatic access, and authentication best practices for securing every call.
@@ -754,3 +757,51 @@ Your APIs are connected and secured. In [Chapter 7: Customization](07-customizat
 ---
 
 *Built with insights from the [Quivr](https://github.com/QuivrHQ/quivr) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `print`, `response`, `question` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Integration APIs` as an operating subsystem inside **Quivr Tutorial: Open-Source RAG Framework for Document Ingestion**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `result`, `quivr`, `sources` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Integration APIs` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `print`.
+2. **Input normalization**: shape incoming data so `response` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `question`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/QuivrHQ/quivr)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `print` and `response` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Knowledge Bases](05-knowledge-bases.md)
+- [Next Chapter: Chapter 7: Customization](07-customization.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/quivr-tutorial/07-customization.md b/tutorials/quivr-tutorial/07-customization.md
index 5d90598e..2486ab4e 100644
--- a/tutorials/quivr-tutorial/07-customization.md
+++ b/tutorials/quivr-tutorial/07-customization.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Customization
 
+Welcome to **Chapter 7: Customization**. In this part of **Quivr Tutorial: Open-Source RAG Framework for Document Ingestion**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 6](06-integration-apis.md), you connected Quivr to external applications through APIs and webhooks. Now it is time to make Quivr truly yours. The default pipeline works well for general use cases, but real-world projects demand customization -- domain-specific preprocessing, specialized reranking, tailored prompt engineering, and branded user interfaces.
 
 This chapter shows you how to extend every layer of the Quivr stack: custom document processors, pluggable embedding and reranking models, configurable prompt templates, feedback-driven learning loops, and frontend customization for chat and search interfaces.
@@ -965,3 +968,51 @@ Your Quivr instance is now fully customized for your domain. In [Chapter 8: Prod
 ---
 
 *Built with insights from the [Quivr](https://github.com/QuivrHQ/quivr) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `text`, `query` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Customization` as an operating subsystem inside **Quivr Tutorial: Open-Source RAG Framework for Document Ingestion**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `quivr`, `print`, `answer` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Customization` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `text` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `query`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/QuivrHQ/quivr)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `text` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Integration APIs](06-integration-apis.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/quivr-tutorial/08-production-deployment.md b/tutorials/quivr-tutorial/08-production-deployment.md
index 4b525529..03af241f 100644
--- a/tutorials/quivr-tutorial/08-production-deployment.md
+++ b/tutorials/quivr-tutorial/08-production-deployment.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **Quivr Tutorial: Open-Source RAG Framework for Document Ingestion**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 7](07-customization.md), you customized Quivr with domain-specific processors, rerankers, prompts, and plugins. Now it is time to take everything to production. A development setup running on localhost is fine for experimentation, but serving real users at scale requires proper containerization, infrastructure design, security hardening, monitoring, and cost management.
 
 This chapter covers the complete journey from a single Docker container to a production-grade deployment: infrastructure architecture, Docker and Kubernetes configurations, database and vector store scaling, security hardening, observability, performance tuning, backup strategies, and a comprehensive go-live checklist.
@@ -983,3 +986,50 @@ Production deployment transforms Quivr from a development tool into an enterpris
 ---
 
 *Built with insights from the [Quivr](https://github.com/QuivrHQ/quivr) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `quivr`, `print`, `name` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **Quivr Tutorial: Open-Source RAG Framework for Document Ingestion**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `report`, `classDef`, `fill` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `quivr`.
+2. **Input normalization**: shape incoming data so `print` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `name`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/QuivrHQ/quivr)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `quivr` and `print` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Customization](07-customization.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/qwen-agent-tutorial/01-getting-started.md b/tutorials/qwen-agent-tutorial/01-getting-started.md
index 568d1073..f8b9a5ce 100644
--- a/tutorials/qwen-agent-tutorial/01-getting-started.md
+++ b/tutorials/qwen-agent-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Qwen-Agent Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Qwen-Agent installed with a first runnable baseline.
 
 ## Learning Goals
@@ -33,3 +36,601 @@ pip install -U "qwen-agent[gui,rag,code_interpreter,mcp]"
 You now have a working Qwen-Agent baseline.
 
 Next: [Chapter 2: Framework Architecture and Core Modules](02-framework-architecture-and-core-modules.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- tutorial slug: **qwen-agent-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Qwen Agent Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Qwen-Agent Repository](https://github.com/QwenLM/Qwen-Agent)
+- [Qwen-Agent README](https://github.com/QwenLM/Qwen-Agent/blob/main/README.md)
+- [Qwen-Agent Docs](https://qwenlm.github.io/Qwen-Agent/en/)
+- [Qwen-Agent Guide](https://qwenlm.github.io/Qwen-Agent/en/guide/)
+- [DeepPlanning Benchmark Page](https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/)
+
+### Cross-Tutorial Connection Map
+
+- [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+- [SWE-agent Tutorial](../swe-agent-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `install`, `qwen`, `agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `code_interpreter` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `install`.
+2. **Input normalization**: shape incoming data so `qwen` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Qwen-Agent Repository](https://github.com/QwenLM/Qwen-Agent)
+  Why it matters: authoritative reference on `Qwen-Agent Repository` (github.com).
+- [Qwen-Agent README](https://github.com/QwenLM/Qwen-Agent/blob/main/README.md)
+  Why it matters: authoritative reference on `Qwen-Agent README` (github.com).
+- [Qwen-Agent Docs](https://qwenlm.github.io/Qwen-Agent/en/)
+  Why it matters: authoritative reference on `Qwen-Agent Docs` (qwenlm.github.io).
+- [Qwen-Agent Guide](https://qwenlm.github.io/Qwen-Agent/en/guide/)
+  Why it matters: authoritative reference on `Qwen-Agent Guide` (qwenlm.github.io).
+- [DeepPlanning Benchmark Page](https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/)
+  Why it matters: authoritative reference on `DeepPlanning Benchmark Page` (qwenlm.github.io).
+
+Suggested trace strategy:
+- search upstream code for `install` and `qwen` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Framework Architecture and Core Modules](02-framework-architecture-and-core-modules.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/qwen-agent-tutorial/02-framework-architecture-and-core-modules.md b/tutorials/qwen-agent-tutorial/02-framework-architecture-and-core-modules.md
index 0c0ac20d..0a93930e 100644
--- a/tutorials/qwen-agent-tutorial/02-framework-architecture-and-core-modules.md
+++ b/tutorials/qwen-agent-tutorial/02-framework-architecture-and-core-modules.md
@@ -7,6 +7,9 @@ parent: Qwen-Agent Tutorial
 
 # Chapter 2: Framework Architecture and Core Modules
 
+Welcome to **Chapter 2: Framework Architecture and Core Modules**. In this part of **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains the internal framework layers and extension surfaces.
 
 ## Learning Goals
@@ -34,3 +37,598 @@ This chapter explains the internal framework layers and extension surfaces.
 You now have a reliable mental model for Qwen-Agent framework internals.
 
 Next: [Chapter 3: Model Service and Runtime Strategy](03-model-service-and-runtime-strategy.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- tutorial slug: **qwen-agent-tutorial**
+- chapter focus: **Chapter 2: Framework Architecture and Core Modules**
+- system context: **Qwen Agent Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Framework Architecture and Core Modules`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Qwen-Agent Repository](https://github.com/QwenLM/Qwen-Agent)
+- [Qwen-Agent README](https://github.com/QwenLM/Qwen-Agent/blob/main/README.md)
+- [Qwen-Agent Docs](https://qwenlm.github.io/Qwen-Agent/en/)
+- [Qwen-Agent Guide](https://qwenlm.github.io/Qwen-Agent/en/guide/)
+- [DeepPlanning Benchmark Page](https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/)
+
+### Cross-Tutorial Connection Map
+
+- [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+- [SWE-agent Tutorial](../swe-agent-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Framework Architecture and Core Modules`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Framework Architecture and Core Modules
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Framework Architecture and Core Modules` as an operating subsystem inside **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Framework Architecture and Core Modules` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Qwen-Agent Repository](https://github.com/QwenLM/Qwen-Agent)
+  Why it matters: authoritative reference on `Qwen-Agent Repository` (github.com).
+- [Qwen-Agent README](https://github.com/QwenLM/Qwen-Agent/blob/main/README.md)
+  Why it matters: authoritative reference on `Qwen-Agent README` (github.com).
+- [Qwen-Agent Docs](https://qwenlm.github.io/Qwen-Agent/en/)
+  Why it matters: authoritative reference on `Qwen-Agent Docs` (qwenlm.github.io).
+- [Qwen-Agent Guide](https://qwenlm.github.io/Qwen-Agent/en/guide/)
+  Why it matters: authoritative reference on `Qwen-Agent Guide` (qwenlm.github.io).
+- [DeepPlanning Benchmark Page](https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/)
+  Why it matters: authoritative reference on `DeepPlanning Benchmark Page` (qwenlm.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Model Service and Runtime Strategy](03-model-service-and-runtime-strategy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/qwen-agent-tutorial/03-model-service-and-runtime-strategy.md b/tutorials/qwen-agent-tutorial/03-model-service-and-runtime-strategy.md
index a852bb5a..14ae712a 100644
--- a/tutorials/qwen-agent-tutorial/03-model-service-and-runtime-strategy.md
+++ b/tutorials/qwen-agent-tutorial/03-model-service-and-runtime-strategy.md
@@ -7,6 +7,9 @@ parent: Qwen-Agent Tutorial
 
 # Chapter 3: Model Service and Runtime Strategy
 
+Welcome to **Chapter 3: Model Service and Runtime Strategy**. In this part of **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers model-serving choices and runtime tradeoffs.
 
 ## Learning Goals
@@ -33,3 +36,598 @@ This chapter covers model-serving choices and runtime tradeoffs.
 You now can pick model service and parser strategies with fewer integration surprises.
 
 Next: [Chapter 4: Tool Calling and MCP Integration](04-tool-calling-and-mcp-integration.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- tutorial slug: **qwen-agent-tutorial**
+- chapter focus: **Chapter 3: Model Service and Runtime Strategy**
+- system context: **Qwen Agent Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Model Service and Runtime Strategy`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Qwen-Agent Repository](https://github.com/QwenLM/Qwen-Agent)
+- [Qwen-Agent README](https://github.com/QwenLM/Qwen-Agent/blob/main/README.md)
+- [Qwen-Agent Docs](https://qwenlm.github.io/Qwen-Agent/en/)
+- [Qwen-Agent Guide](https://qwenlm.github.io/Qwen-Agent/en/guide/)
+- [DeepPlanning Benchmark Page](https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/)
+
+### Cross-Tutorial Connection Map
+
+- [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+- [SWE-agent Tutorial](../swe-agent-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Model Service and Runtime Strategy`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Model Service and Runtime Strategy
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Model Service and Runtime Strategy` as an operating subsystem inside **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Model Service and Runtime Strategy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Qwen-Agent Repository](https://github.com/QwenLM/Qwen-Agent)
+  Why it matters: authoritative reference on `Qwen-Agent Repository` (github.com).
+- [Qwen-Agent README](https://github.com/QwenLM/Qwen-Agent/blob/main/README.md)
+  Why it matters: authoritative reference on `Qwen-Agent README` (github.com).
+- [Qwen-Agent Docs](https://qwenlm.github.io/Qwen-Agent/en/)
+  Why it matters: authoritative reference on `Qwen-Agent Docs` (qwenlm.github.io).
+- [Qwen-Agent Guide](https://qwenlm.github.io/Qwen-Agent/en/guide/)
+  Why it matters: authoritative reference on `Qwen-Agent Guide` (qwenlm.github.io).
+- [DeepPlanning Benchmark Page](https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/)
+  Why it matters: authoritative reference on `DeepPlanning Benchmark Page` (qwenlm.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Framework Architecture and Core Modules](02-framework-architecture-and-core-modules.md)
+- [Next Chapter: Chapter 4: Tool Calling and MCP Integration](04-tool-calling-and-mcp-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/qwen-agent-tutorial/04-tool-calling-and-mcp-integration.md b/tutorials/qwen-agent-tutorial/04-tool-calling-and-mcp-integration.md
index 0ce9a8c4..d408f9fa 100644
--- a/tutorials/qwen-agent-tutorial/04-tool-calling-and-mcp-integration.md
+++ b/tutorials/qwen-agent-tutorial/04-tool-calling-and-mcp-integration.md
@@ -7,6 +7,9 @@ parent: Qwen-Agent Tutorial
 
 # Chapter 4: Tool Calling and MCP Integration
 
+Welcome to **Chapter 4: Tool Calling and MCP Integration**. In this part of **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains capability expansion through tools and MCP services.
 
 ## Learning Goals
@@ -33,3 +36,598 @@ This chapter explains capability expansion through tools and MCP services.
 You now have a practical model for tool + MCP integration in Qwen-Agent.
 
 Next: [Chapter 5: Memory, RAG, and Long-Context Workflows](05-memory-rag-and-long-context-workflows.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- tutorial slug: **qwen-agent-tutorial**
+- chapter focus: **Chapter 4: Tool Calling and MCP Integration**
+- system context: **Qwen Agent Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Tool Calling and MCP Integration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Qwen-Agent Repository](https://github.com/QwenLM/Qwen-Agent)
+- [Qwen-Agent README](https://github.com/QwenLM/Qwen-Agent/blob/main/README.md)
+- [Qwen-Agent Docs](https://qwenlm.github.io/Qwen-Agent/en/)
+- [Qwen-Agent Guide](https://qwenlm.github.io/Qwen-Agent/en/guide/)
+- [DeepPlanning Benchmark Page](https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/)
+
+### Cross-Tutorial Connection Map
+
+- [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+- [SWE-agent Tutorial](../swe-agent-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Tool Calling and MCP Integration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Tool Calling and MCP Integration
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Tool Calling and MCP Integration` as an operating subsystem inside **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Tool Calling and MCP Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Qwen-Agent Repository](https://github.com/QwenLM/Qwen-Agent)
+  Why it matters: authoritative reference on `Qwen-Agent Repository` (github.com).
+- [Qwen-Agent README](https://github.com/QwenLM/Qwen-Agent/blob/main/README.md)
+  Why it matters: authoritative reference on `Qwen-Agent README` (github.com).
+- [Qwen-Agent Docs](https://qwenlm.github.io/Qwen-Agent/en/)
+  Why it matters: authoritative reference on `Qwen-Agent Docs` (qwenlm.github.io).
+- [Qwen-Agent Guide](https://qwenlm.github.io/Qwen-Agent/en/guide/)
+  Why it matters: authoritative reference on `Qwen-Agent Guide` (qwenlm.github.io).
+- [DeepPlanning Benchmark Page](https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/)
+  Why it matters: authoritative reference on `DeepPlanning Benchmark Page` (qwenlm.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Model Service and Runtime Strategy](03-model-service-and-runtime-strategy.md)
+- [Next Chapter: Chapter 5: Memory, RAG, and Long-Context Workflows](05-memory-rag-and-long-context-workflows.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/qwen-agent-tutorial/05-memory-rag-and-long-context-workflows.md b/tutorials/qwen-agent-tutorial/05-memory-rag-and-long-context-workflows.md
index 36d962b9..dbdb9206 100644
--- a/tutorials/qwen-agent-tutorial/05-memory-rag-and-long-context-workflows.md
+++ b/tutorials/qwen-agent-tutorial/05-memory-rag-and-long-context-workflows.md
@@ -7,6 +7,9 @@ parent: Qwen-Agent Tutorial
 
 # Chapter 5: Memory, RAG, and Long-Context Workflows
 
+Welcome to **Chapter 5: Memory, RAG, and Long-Context Workflows**. In this part of **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers knowledge-heavy workflows requiring retrieval and long-context handling.
 
 ## Learning Goals
@@ -33,3 +36,598 @@ This chapter covers knowledge-heavy workflows requiring retrieval and long-conte
 You now can design Qwen-Agent workflows for high-context and document-heavy workloads.
 
 Next: [Chapter 6: Application Patterns and Safety Boundaries](06-application-patterns-and-safety-boundaries.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- tutorial slug: **qwen-agent-tutorial**
+- chapter focus: **Chapter 5: Memory, RAG, and Long-Context Workflows**
+- system context: **Qwen Agent Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Memory, RAG, and Long-Context Workflows`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Qwen-Agent Repository](https://github.com/QwenLM/Qwen-Agent)
+- [Qwen-Agent README](https://github.com/QwenLM/Qwen-Agent/blob/main/README.md)
+- [Qwen-Agent Docs](https://qwenlm.github.io/Qwen-Agent/en/)
+- [Qwen-Agent Guide](https://qwenlm.github.io/Qwen-Agent/en/guide/)
+- [DeepPlanning Benchmark Page](https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/)
+
+### Cross-Tutorial Connection Map
+
+- [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+- [SWE-agent Tutorial](../swe-agent-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Memory, RAG, and Long-Context Workflows`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Memory, RAG, and Long-Context Workflows
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Memory, RAG, and Long-Context Workflows` as an operating subsystem inside **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Memory, RAG, and Long-Context Workflows` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Qwen-Agent Repository](https://github.com/QwenLM/Qwen-Agent)
+  Why it matters: authoritative reference on `Qwen-Agent Repository` (github.com).
+- [Qwen-Agent README](https://github.com/QwenLM/Qwen-Agent/blob/main/README.md)
+  Why it matters: authoritative reference on `Qwen-Agent README` (github.com).
+- [Qwen-Agent Docs](https://qwenlm.github.io/Qwen-Agent/en/)
+  Why it matters: authoritative reference on `Qwen-Agent Docs` (qwenlm.github.io).
+- [Qwen-Agent Guide](https://qwenlm.github.io/Qwen-Agent/en/guide/)
+  Why it matters: authoritative reference on `Qwen-Agent Guide` (qwenlm.github.io).
+- [DeepPlanning Benchmark Page](https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/)
+  Why it matters: authoritative reference on `DeepPlanning Benchmark Page` (qwenlm.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Tool Calling and MCP Integration](04-tool-calling-and-mcp-integration.md)
+- [Next Chapter: Chapter 6: Application Patterns and Safety Boundaries](06-application-patterns-and-safety-boundaries.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/qwen-agent-tutorial/06-application-patterns-and-safety-boundaries.md b/tutorials/qwen-agent-tutorial/06-application-patterns-and-safety-boundaries.md
index 7ddaef7b..86ed96d2 100644
--- a/tutorials/qwen-agent-tutorial/06-application-patterns-and-safety-boundaries.md
+++ b/tutorials/qwen-agent-tutorial/06-application-patterns-and-safety-boundaries.md
@@ -7,6 +7,9 @@ parent: Qwen-Agent Tutorial
 
 # Chapter 6: Application Patterns and Safety Boundaries
 
+Welcome to **Chapter 6: Application Patterns and Safety Boundaries**. In this part of **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter maps application-level patterns and operational caveats.
 
 ## Learning Goals
@@ -33,3 +36,598 @@ This chapter maps application-level patterns and operational caveats.
 You now have a safer application-design lens for Qwen-Agent deployments.
 
 Next: [Chapter 7: Benchmarking and DeepPlanning Evaluation](07-benchmarking-and-deepplanning-evaluation.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- tutorial slug: **qwen-agent-tutorial**
+- chapter focus: **Chapter 6: Application Patterns and Safety Boundaries**
+- system context: **Qwen Agent Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Application Patterns and Safety Boundaries`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Qwen-Agent Repository](https://github.com/QwenLM/Qwen-Agent)
+- [Qwen-Agent README](https://github.com/QwenLM/Qwen-Agent/blob/main/README.md)
+- [Qwen-Agent Docs](https://qwenlm.github.io/Qwen-Agent/en/)
+- [Qwen-Agent Guide](https://qwenlm.github.io/Qwen-Agent/en/guide/)
+- [DeepPlanning Benchmark Page](https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/)
+
+### Cross-Tutorial Connection Map
+
+- [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+- [SWE-agent Tutorial](../swe-agent-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Application Patterns and Safety Boundaries`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Application Patterns and Safety Boundaries
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Application Patterns and Safety Boundaries` as an operating subsystem inside **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Application Patterns and Safety Boundaries` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Qwen-Agent Repository](https://github.com/QwenLM/Qwen-Agent)
+  Why it matters: authoritative reference on `Qwen-Agent Repository` (github.com).
+- [Qwen-Agent README](https://github.com/QwenLM/Qwen-Agent/blob/main/README.md)
+  Why it matters: authoritative reference on `Qwen-Agent README` (github.com).
+- [Qwen-Agent Docs](https://qwenlm.github.io/Qwen-Agent/en/)
+  Why it matters: authoritative reference on `Qwen-Agent Docs` (qwenlm.github.io).
+- [Qwen-Agent Guide](https://qwenlm.github.io/Qwen-Agent/en/guide/)
+  Why it matters: authoritative reference on `Qwen-Agent Guide` (qwenlm.github.io).
+- [DeepPlanning Benchmark Page](https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/)
+  Why it matters: authoritative reference on `DeepPlanning Benchmark Page` (qwenlm.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Memory, RAG, and Long-Context Workflows](05-memory-rag-and-long-context-workflows.md)
+- [Next Chapter: Chapter 7: Benchmarking and DeepPlanning Evaluation](07-benchmarking-and-deepplanning-evaluation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/qwen-agent-tutorial/07-benchmarking-and-deepplanning-evaluation.md b/tutorials/qwen-agent-tutorial/07-benchmarking-and-deepplanning-evaluation.md
index 366583a7..698d7673 100644
--- a/tutorials/qwen-agent-tutorial/07-benchmarking-and-deepplanning-evaluation.md
+++ b/tutorials/qwen-agent-tutorial/07-benchmarking-and-deepplanning-evaluation.md
@@ -7,6 +7,9 @@ parent: Qwen-Agent Tutorial
 
 # Chapter 7: Benchmarking and DeepPlanning Evaluation
 
+Welcome to **Chapter 7: Benchmarking and DeepPlanning Evaluation**. In this part of **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on long-horizon planning benchmarks and evaluation quality.
 
 ## Learning Goals
@@ -33,3 +36,598 @@ This chapter focuses on long-horizon planning benchmarks and evaluation quality.
 You now have a benchmark-driven evaluation model for long-horizon Qwen-Agent tasks.
 
 Next: [Chapter 8: Contribution Workflow and Production Governance](08-contribution-workflow-and-production-governance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- tutorial slug: **qwen-agent-tutorial**
+- chapter focus: **Chapter 7: Benchmarking and DeepPlanning Evaluation**
+- system context: **Qwen Agent Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Benchmarking and DeepPlanning Evaluation`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Qwen-Agent Repository](https://github.com/QwenLM/Qwen-Agent)
+- [Qwen-Agent README](https://github.com/QwenLM/Qwen-Agent/blob/main/README.md)
+- [Qwen-Agent Docs](https://qwenlm.github.io/Qwen-Agent/en/)
+- [Qwen-Agent Guide](https://qwenlm.github.io/Qwen-Agent/en/guide/)
+- [DeepPlanning Benchmark Page](https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/)
+
+### Cross-Tutorial Connection Map
+
+- [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+- [SWE-agent Tutorial](../swe-agent-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Benchmarking and DeepPlanning Evaluation`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Benchmarking and DeepPlanning Evaluation
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Benchmarking and DeepPlanning Evaluation` as an operating subsystem inside **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Benchmarking and DeepPlanning Evaluation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Qwen-Agent Repository](https://github.com/QwenLM/Qwen-Agent)
+  Why it matters: authoritative reference on `Qwen-Agent Repository` (github.com).
+- [Qwen-Agent README](https://github.com/QwenLM/Qwen-Agent/blob/main/README.md)
+  Why it matters: authoritative reference on `Qwen-Agent README` (github.com).
+- [Qwen-Agent Docs](https://qwenlm.github.io/Qwen-Agent/en/)
+  Why it matters: authoritative reference on `Qwen-Agent Docs` (qwenlm.github.io).
+- [Qwen-Agent Guide](https://qwenlm.github.io/Qwen-Agent/en/guide/)
+  Why it matters: authoritative reference on `Qwen-Agent Guide` (qwenlm.github.io).
+- [DeepPlanning Benchmark Page](https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/)
+  Why it matters: authoritative reference on `DeepPlanning Benchmark Page` (qwenlm.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Application Patterns and Safety Boundaries](06-application-patterns-and-safety-boundaries.md)
+- [Next Chapter: Chapter 8: Contribution Workflow and Production Governance](08-contribution-workflow-and-production-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/qwen-agent-tutorial/08-contribution-workflow-and-production-governance.md b/tutorials/qwen-agent-tutorial/08-contribution-workflow-and-production-governance.md
index 6898dff1..3f84fc25 100644
--- a/tutorials/qwen-agent-tutorial/08-contribution-workflow-and-production-governance.md
+++ b/tutorials/qwen-agent-tutorial/08-contribution-workflow-and-production-governance.md
@@ -7,6 +7,9 @@ parent: Qwen-Agent Tutorial
 
 # Chapter 8: Contribution Workflow and Production Governance
 
+Welcome to **Chapter 8: Contribution Workflow and Production Governance**. In this part of **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter closes with contribution strategy and team governance patterns.
 
 ## Learning Goals
@@ -35,3 +38,597 @@ This chapter closes with contribution strategy and team governance patterns.
 You now have a complete Qwen-Agent path from first setup to production governance.
 
 Next tutorial: [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- tutorial slug: **qwen-agent-tutorial**
+- chapter focus: **Chapter 8: Contribution Workflow and Production Governance**
+- system context: **Qwen Agent Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Contribution Workflow and Production Governance`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Qwen-Agent Repository](https://github.com/QwenLM/Qwen-Agent)
+- [Qwen-Agent README](https://github.com/QwenLM/Qwen-Agent/blob/main/README.md)
+- [Qwen-Agent Docs](https://qwenlm.github.io/Qwen-Agent/en/)
+- [Qwen-Agent Guide](https://qwenlm.github.io/Qwen-Agent/en/guide/)
+- [DeepPlanning Benchmark Page](https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/)
+
+### Cross-Tutorial Connection Map
+
+- [Mini-SWE-Agent Tutorial](../mini-swe-agent-tutorial/)
+- [SWE-agent Tutorial](../swe-agent-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Contribution Workflow and Production Governance`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Contribution Workflow and Production Governance
+
+- tutorial context: **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Contribution Workflow and Production Governance` as an operating subsystem inside **Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Contribution Workflow and Production Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Qwen-Agent Repository](https://github.com/QwenLM/Qwen-Agent)
+  Why it matters: authoritative reference on `Qwen-Agent Repository` (github.com).
+- [Qwen-Agent README](https://github.com/QwenLM/Qwen-Agent/blob/main/README.md)
+  Why it matters: authoritative reference on `Qwen-Agent README` (github.com).
+- [Qwen-Agent Docs](https://qwenlm.github.io/Qwen-Agent/en/)
+  Why it matters: authoritative reference on `Qwen-Agent Docs` (qwenlm.github.io).
+- [Qwen-Agent Guide](https://qwenlm.github.io/Qwen-Agent/en/guide/)
+  Why it matters: authoritative reference on `Qwen-Agent Guide` (qwenlm.github.io).
+- [DeepPlanning Benchmark Page](https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/)
+  Why it matters: authoritative reference on `DeepPlanning Benchmark Page` (qwenlm.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Benchmarking and DeepPlanning Evaluation](07-benchmarking-and-deepplanning-evaluation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/ragflow-tutorial/01-getting-started.md b/tutorials/ragflow-tutorial/01-getting-started.md
index adf903b9..7ccce887 100644
--- a/tutorials/ragflow-tutorial/01-getting-started.md
+++ b/tutorials/ragflow-tutorial/01-getting-started.md
@@ -502,3 +502,50 @@ Now that you have RAGFlow up and running, let's dive deeper into document proces
 5. Set up monitoring for your RAGFlow instance
 
 *What document type are you most excited to process with RAGFlow?* 📄
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `ragflow`, `docker`, `compose` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with RAGFlow` as an operating subsystem inside **RAGFlow Tutorial: Complete Guide to Open-Source RAG Engine**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `mysql`, `response`, `Check` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with RAGFlow` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `ragflow`.
+2. **Input normalization**: shape incoming data so `docker` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `compose`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/infiniflow/ragflow)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `ragflow` and `docker` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Document Processing](02-document-processing.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/ragflow-tutorial/02-document-processing.md b/tutorials/ragflow-tutorial/02-document-processing.md
index c54cea37..45d6bf99 100644
--- a/tutorials/ragflow-tutorial/02-document-processing.md
+++ b/tutorials/ragflow-tutorial/02-document-processing.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Document Processing
 
+Welcome to **Chapter 2: Document Processing**. In this part of **RAGFlow Tutorial: Complete Guide to Open-Source RAG Engine**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explores RAGFlow's powerful document processing capabilities. You'll learn how to upload, parse, and optimize documents from various formats for maximum retrieval performance.
 
 ## 🎯 What You'll Learn
@@ -724,3 +727,51 @@ Ready to configure your knowledge bases? Let's explore [Chapter 3: Knowledge Bas
 5. Optimize chunk sizes for your use case
 
 *What's the most challenging document type you've processed?* 📄
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `text`, `file_path` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Document Processing` as an operating subsystem inside **RAGFlow Tutorial: Complete Guide to Open-Source RAG Engine**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `content`, `current_chunk`, `sentence` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Document Processing` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `text` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `file_path`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/infiniflow/ragflow)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `text` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with RAGFlow](01-getting-started.md)
+- [Next Chapter: Chapter 3: Knowledge Base Setup](03-knowledge-base-setup.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/ragflow-tutorial/03-knowledge-base-setup.md b/tutorials/ragflow-tutorial/03-knowledge-base-setup.md
index 8eaaa763..a9af12b8 100644
--- a/tutorials/ragflow-tutorial/03-knowledge-base-setup.md
+++ b/tutorials/ragflow-tutorial/03-knowledge-base-setup.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Knowledge Base Setup
 
+Welcome to **Chapter 3: Knowledge Base Setup**. In this part of **RAGFlow Tutorial: Complete Guide to Open-Source RAG Engine**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter dives deep into creating and configuring knowledge bases in RAGFlow. You'll learn how to optimize knowledge bases for different use cases, configure embedding models, and fine-tune retrieval settings.
 
 ## 🎯 What You'll Learn
@@ -647,3 +650,51 @@ Ready to dive into retrieval systems? Let's explore [Chapter 4: Retrieval System
 5. Experiment with chunk size optimization
 
 *What's the most important factor for your knowledge base performance?* 🧠
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `List`, `query` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Knowledge Base Setup` as an operating subsystem inside **RAGFlow Tutorial: Complete Guide to Open-Source RAG Engine**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `embedding`, `text`, `metrics` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Knowledge Base Setup` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `List` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `query`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/infiniflow/ragflow)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `List` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Document Processing](02-document-processing.md)
+- [Next Chapter: Chapter 4: Retrieval System](04-retrieval-system.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/ragflow-tutorial/04-retrieval-system.md b/tutorials/ragflow-tutorial/04-retrieval-system.md
index 1211d52b..9841fdc5 100644
--- a/tutorials/ragflow-tutorial/04-retrieval-system.md
+++ b/tutorials/ragflow-tutorial/04-retrieval-system.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Retrieval System
 
+Welcome to **Chapter 4: Retrieval System**. In this part of **RAGFlow Tutorial: Complete Guide to Open-Source RAG Engine**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explores advanced retrieval techniques in RAGFlow. You'll learn about hybrid search, reranking, query expansion, and other sophisticated methods to improve retrieval quality and relevance.
 
 ## 🎯 What You'll Learn
@@ -764,3 +767,51 @@ Ready to build chatbots? Let's explore [Chapter 5: LLM Integration](05-llm-integ
 5. Set up A/B testing for retrieval improvements
 
 *What's the most challenging retrieval scenario you've encountered?* 🔍
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `query`, `documents` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Retrieval System` as an operating subsystem inside **RAGFlow Tutorial: Complete Guide to Open-Source RAG Engine**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `results`, `List`, `Dict` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Retrieval System` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `query` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `documents`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/infiniflow/ragflow)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `query` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Knowledge Base Setup](03-knowledge-base-setup.md)
+- [Next Chapter: Chapter 5: LLM Integration & Configuration](05-llm-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/ragflow-tutorial/05-llm-integration.md b/tutorials/ragflow-tutorial/05-llm-integration.md
index 888be3e6..38fd6201 100644
--- a/tutorials/ragflow-tutorial/05-llm-integration.md
+++ b/tutorials/ragflow-tutorial/05-llm-integration.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: LLM Integration & Configuration
 
+Welcome to **Chapter 5: LLM Integration & Configuration**. In this part of **RAGFlow Tutorial: Complete Guide to Open-Source RAG Engine**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Connect RAGFlow with various Large Language Models for intelligent question answering.
 
 ## 🎯 Overview
@@ -394,4 +397,52 @@ Now that you have configured LLMs for RAGFlow, you're ready to:
 
 ---
 
-**Ready to build intelligent chatbots? Continue to [Chapter 6: Chatbot Development](06-chatbot-development.md)!** 🚀
\ No newline at end of file
+**Ready to build intelligent chatbots? Continue to [Chapter 6: Chatbot Development](06-chatbot-development.md)!** 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `model`, `temperature`, `provider` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: LLM Integration & Configuration` as an operating subsystem inside **RAGFlow Tutorial: Complete Guide to Open-Source RAG Engine**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `max_tokens`, `your`, `claude` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: LLM Integration & Configuration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `model`.
+2. **Input normalization**: shape incoming data so `temperature` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `provider`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/infiniflow/ragflow)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `model` and `temperature` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Retrieval System](04-retrieval-system.md)
+- [Next Chapter: Chapter 6: Chatbot Development](06-chatbot-development.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/ragflow-tutorial/06-chatbot-development.md b/tutorials/ragflow-tutorial/06-chatbot-development.md
index 55a4e257..1e15bf70 100644
--- a/tutorials/ragflow-tutorial/06-chatbot-development.md
+++ b/tutorials/ragflow-tutorial/06-chatbot-development.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Chatbot Development
 
+Welcome to **Chapter 6: Chatbot Development**. In this part of **RAGFlow Tutorial: Complete Guide to Open-Source RAG Engine**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Build intelligent conversational interfaces that leverage your document knowledge bases.
 
 ## 🎯 Overview
@@ -621,4 +624,52 @@ With your chatbot developed, you're ready to:
 
 ---
 
-**Ready to explore advanced features? Continue to [Chapter 7: Advanced Features](07-advanced-features.md)!** 🚀
\ No newline at end of file
+**Ready to explore advanced features? Continue to [Chapter 7: Advanced Features](07-advanced-features.md)!** 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `message`, `context` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Chatbot Development` as an operating subsystem inside **RAGFlow Tutorial: Complete Guide to Open-Source RAG Engine**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `response`, `user`, `__init__` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Chatbot Development` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `message` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `context`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/infiniflow/ragflow)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `message` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: LLM Integration & Configuration](05-llm-integration.md)
+- [Next Chapter: Chapter 7: Advanced Features](07-advanced-features.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/ragflow-tutorial/07-advanced-features.md b/tutorials/ragflow-tutorial/07-advanced-features.md
index 7f55f3e5..34b72f61 100644
--- a/tutorials/ragflow-tutorial/07-advanced-features.md
+++ b/tutorials/ragflow-tutorial/07-advanced-features.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Advanced Features
 
+Welcome to **Chapter 7: Advanced Features**. In this part of **RAGFlow Tutorial: Complete Guide to Open-Source RAG Engine**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master advanced RAGFlow capabilities including custom models, multi-modal processing, and specialized workflows.
 
 ## 🎯 Overview
@@ -735,4 +738,52 @@ With advanced features mastered, you're ready for:
 
 ---
 
-**Ready for production deployment? Continue to [Chapter 8: Production Deployment](08-production-deployment.md)!** 🚀
\ No newline at end of file
+**Ready for production deployment? Continue to [Chapter 8: Production Deployment](08-production-deployment.md)!** 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `metrics`, `document` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Advanced Features` as an operating subsystem inside **RAGFlow Tutorial: Complete Guide to Open-Source RAG Engine**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `text`, `texts`, `insights` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Advanced Features` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `metrics` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `document`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/infiniflow/ragflow)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `metrics` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Chatbot Development](06-chatbot-development.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/ragflow-tutorial/08-production-deployment.md b/tutorials/ragflow-tutorial/08-production-deployment.md
index a70a22e3..411a8a41 100644
--- a/tutorials/ragflow-tutorial/08-production-deployment.md
+++ b/tutorials/ragflow-tutorial/08-production-deployment.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **RAGFlow Tutorial: Complete Guide to Open-Source RAG Engine**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy RAGFlow at enterprise scale with high availability, monitoring, and security best practices.
 
 ## 🎯 Overview
@@ -1239,3 +1242,50 @@ You've successfully completed the comprehensive RAGFlow tutorial! 🎉
 ---
 
 **Thank you for completing this comprehensive RAGFlow tutorial! Your journey to building intelligent document Q&A systems has just begun. 🚀**
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `ragflow`, `name` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **RAGFlow Tutorial: Complete Guide to Open-Source RAG Engine**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `primary`, `redis`, `metrics` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `ragflow` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `name`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/infiniflow/ragflow)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `ragflow` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Advanced Features](07-advanced-features.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/react-fiber-internals/01-introduction.md b/tutorials/react-fiber-internals/01-introduction.md
index daaa0124..5bd0f5ec 100644
--- a/tutorials/react-fiber-internals/01-introduction.md
+++ b/tutorials/react-fiber-internals/01-introduction.md
@@ -7,6 +7,9 @@ nav_order: 1
 
 # Chapter 1: Introduction to Fiber
 
+Welcome to **Chapter 1: Introduction to Fiber**. In this part of **React Fiber Internals**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Understand why React needed Fiber, the problems it solves, and how it fundamentally changed React's architecture.
 
 ## Overview
@@ -402,3 +405,48 @@ Now that you understand why Fiber exists, let's dive deep into the Fiber data st
 **Ready for Chapter 2?** [Fiber Data Structure](02-fiber-structure.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `fiber`, `input`, `items` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Introduction to Fiber` as an operating subsystem inside **React Fiber Internals**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `work`, `next`, `item` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Introduction to Fiber` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `fiber`.
+2. **Input normalization**: shape incoming data so `input` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `items`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `fiber` and `input` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Fiber Data Structure](02-fiber-structure.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/react-fiber-internals/02-fiber-structure.md b/tutorials/react-fiber-internals/02-fiber-structure.md
index 33022c67..403c3211 100644
--- a/tutorials/react-fiber-internals/02-fiber-structure.md
+++ b/tutorials/react-fiber-internals/02-fiber-structure.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Fiber Data Structure
 
+Welcome to **Chapter 2: Fiber Data Structure**. In this part of **React Fiber Internals**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deep dive into the Fiber node structure, its properties, and how the tree is organized.
 
 ## Overview
@@ -431,3 +434,49 @@ Now that you understand the Fiber structure, let's explore how React builds the
 **Ready for Chapter 3?** [Render Phase](03-render-phase.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `fiber`, `current`, `child` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Fiber Data Structure` as an operating subsystem inside **React Fiber Internals**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `React`, `workInProgress`, `Fiber` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Fiber Data Structure` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `fiber`.
+2. **Input normalization**: shape incoming data so `current` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `child`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `fiber` and `current` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Introduction to Fiber](01-introduction.md)
+- [Next Chapter: Chapter 3: Render Phase](03-render-phase.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/react-fiber-internals/03-render-phase.md b/tutorials/react-fiber-internals/03-render-phase.md
index 08e82000..3725e57b 100644
--- a/tutorials/react-fiber-internals/03-render-phase.md
+++ b/tutorials/react-fiber-internals/03-render-phase.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Render Phase
 
+Welcome to **Chapter 3: Render Phase**. In this part of **React Fiber Internals**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Understanding how React builds the work-in-progress tree through beginWork and completeWork.
 
 ## Overview
@@ -717,3 +720,49 @@ Now that you understand how React builds the work-in-progress tree, let's explor
 **Ready for Chapter 4?** [Commit Phase](04-commit-phase.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `workInProgress`, `current`, `child` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Render Phase` as an operating subsystem inside **React Fiber Internals**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `renderLanes`, `returnFiber`, `node` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Render Phase` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `workInProgress`.
+2. **Input normalization**: shape incoming data so `current` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `child`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `workInProgress` and `current` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Fiber Data Structure](02-fiber-structure.md)
+- [Next Chapter: Chapter 4: Commit Phase](04-commit-phase.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/react-fiber-internals/04-commit-phase.md b/tutorials/react-fiber-internals/04-commit-phase.md
index e5751724..e371ecaf 100644
--- a/tutorials/react-fiber-internals/04-commit-phase.md
+++ b/tutorials/react-fiber-internals/04-commit-phase.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Commit Phase
 
+Welcome to **Chapter 4: Commit Phase**. In this part of **React Fiber Internals**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > How React applies changes to the DOM and executes side effects.
 
 ## Overview
@@ -732,3 +735,49 @@ Now that you understand how changes are committed, let's explore how React sched
 **Ready for Chapter 5?** [Scheduling and Lanes](05-scheduling.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `finishedWork`, `root`, `fiber` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Commit Phase` as an operating subsystem inside **React Fiber Internals**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `flags`, `nextEffect`, `instance` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Commit Phase` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `finishedWork`.
+2. **Input normalization**: shape incoming data so `root` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `fiber`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `finishedWork` and `root` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Render Phase](03-render-phase.md)
+- [Next Chapter: Chapter 5: Scheduling and Lanes](05-scheduling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/react-fiber-internals/05-scheduling.md b/tutorials/react-fiber-internals/05-scheduling.md
index 9ddefd18..b39b042f 100644
--- a/tutorials/react-fiber-internals/05-scheduling.md
+++ b/tutorials/react-fiber-internals/05-scheduling.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Scheduling and Lanes
 
+Welcome to **Chapter 5: Scheduling and Lanes**. In this part of **React Fiber Internals**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Understanding React's priority system, the Scheduler, and how lanes enable concurrent rendering.
 
 ## Overview
@@ -595,3 +598,49 @@ Now that you understand scheduling and lanes, let's explore how hooks work inter
 **Ready for Chapter 6?** [Hooks Implementation](06-hooks.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `root`, `lanes`, `lane` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Scheduling and Lanes` as an operating subsystem inside **React Fiber Internals**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `priority`, `pendingLanes`, `work` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Scheduling and Lanes` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `root`.
+2. **Input normalization**: shape incoming data so `lanes` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `lane`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `root` and `lanes` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Commit Phase](04-commit-phase.md)
+- [Next Chapter: Chapter 6: Hooks Implementation](06-hooks.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/react-fiber-internals/06-hooks.md b/tutorials/react-fiber-internals/06-hooks.md
index 8787cc29..1bde6d42 100644
--- a/tutorials/react-fiber-internals/06-hooks.md
+++ b/tutorials/react-fiber-internals/06-hooks.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Hooks Implementation
 
+Welcome to **Chapter 6: Hooks Implementation**. In this part of **React Fiber Internals**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Understanding how React implements hooks internally using linked lists and the fiber's memoizedState.
 
 ## Overview
@@ -714,3 +717,49 @@ Now that you understand hooks implementation, let's explore concurrent rendering
 **Ready for Chapter 7?** [Concurrent Features](07-concurrent.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `hook`, `next`, `memoizedState` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Hooks Implementation` as an operating subsystem inside **React Fiber Internals**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `queue`, `deps`, `update` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Hooks Implementation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `hook`.
+2. **Input normalization**: shape incoming data so `next` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `memoizedState`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `hook` and `next` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Scheduling and Lanes](05-scheduling.md)
+- [Next Chapter: Chapter 7: Concurrent Features](07-concurrent.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/react-fiber-internals/07-concurrent.md b/tutorials/react-fiber-internals/07-concurrent.md
index 9259c5c7..a0ab4b4a 100644
--- a/tutorials/react-fiber-internals/07-concurrent.md
+++ b/tutorials/react-fiber-internals/07-concurrent.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Concurrent Features
 
+Welcome to **Chapter 7: Concurrent Features**. In this part of **React Fiber Internals**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Understanding Suspense, transitions, and other concurrent rendering features in React.
 
 ## Overview
@@ -619,3 +622,49 @@ Now that you understand concurrent features, let's explore debugging and profili
 **Ready for Chapter 8?** [Debugging and Profiling](08-debugging.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `workInProgress`, `root`, `mode` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Concurrent Features` as an operating subsystem inside **React Fiber Internals**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `lanes`, `Suspense`, `memoizedState` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Concurrent Features` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `workInProgress`.
+2. **Input normalization**: shape incoming data so `root` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `mode`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `workInProgress` and `root` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Hooks Implementation](06-hooks.md)
+- [Next Chapter: Chapter 8: Debugging and Profiling](08-debugging.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/react-fiber-internals/08-debugging.md b/tutorials/react-fiber-internals/08-debugging.md
index 449b1a73..d68c7ba1 100644
--- a/tutorials/react-fiber-internals/08-debugging.md
+++ b/tutorials/react-fiber-internals/08-debugging.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Debugging and Profiling
 
+Welcome to **Chapter 8: Debugging and Profiling**. In this part of **React Fiber Internals**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Tools and techniques for debugging React internals and profiling performance.
 
 ## Overview
@@ -624,3 +627,48 @@ Congratulations! You've completed the React Fiber Internals tutorial. You now un
 ---
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `fiber`, `current`, `workInProgress` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Debugging and Profiling` as an operating subsystem inside **React Fiber Internals**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `hook`, `name`, `console` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Debugging and Profiling` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `fiber`.
+2. **Input normalization**: shape incoming data so `current` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `workInProgress`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `fiber` and `current` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Concurrent Features](07-concurrent.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/refly-tutorial/01-getting-started.md b/tutorials/refly-tutorial/01-getting-started.md
index f70b463a..051aeb08 100644
--- a/tutorials/refly-tutorial/01-getting-started.md
+++ b/tutorials/refly-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Refly Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter establishes a local Refly baseline for experimentation and integration.
 
 ## Learning Goals
@@ -44,3 +47,601 @@ pnpm dev
 You now have a baseline local environment for running Refly workflows.
 
 Next: [Chapter 2: Architecture and Component Topology](02-architecture-and-component-topology.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- tutorial slug: **refly-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Refly Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Refly Repository](https://github.com/refly-ai/refly)
+- [README](https://github.com/refly-ai/refly/blob/main/README.md)
+- [API Guide (OpenAPI)](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/openapi.md)
+- [Webhook Guide](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/webhook.md)
+- [Contributing Guide](https://github.com/refly-ai/refly/blob/main/CONTRIBUTING.md)
+
+### Cross-Tutorial Connection Map
+
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [n8n AI Tutorial](../n8n-ai-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `pnpm`, `docker`, `compose` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `deploy`, `middleware`, `refly` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `pnpm`.
+2. **Input normalization**: shape incoming data so `docker` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `compose`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Refly Repository](https://github.com/refly-ai/refly)
+  Why it matters: authoritative reference on `Refly Repository` (github.com).
+- [README](https://github.com/refly-ai/refly/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [API Guide (OpenAPI)](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/openapi.md)
+  Why it matters: authoritative reference on `API Guide (OpenAPI)` (github.com).
+- [Webhook Guide](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/webhook.md)
+  Why it matters: authoritative reference on `Webhook Guide` (github.com).
+- [Contributing Guide](https://github.com/refly-ai/refly/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `pnpm` and `docker` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Architecture and Component Topology](02-architecture-and-component-topology.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/refly-tutorial/02-architecture-and-component-topology.md b/tutorials/refly-tutorial/02-architecture-and-component-topology.md
index c547c067..5a22a52d 100644
--- a/tutorials/refly-tutorial/02-architecture-and-component-topology.md
+++ b/tutorials/refly-tutorial/02-architecture-and-component-topology.md
@@ -7,6 +7,9 @@ parent: Refly Tutorial
 
 # Chapter 2: Architecture and Component Topology
 
+Welcome to **Chapter 2: Architecture and Component Topology**. In this part of **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter maps Refly's monorepo into runtime responsibilities.
 
 ## Learning Goals
@@ -36,3 +39,598 @@ This chapter maps Refly's monorepo into runtime responsibilities.
 You now understand the architectural boundaries and extension points in Refly.
 
 Next: [Chapter 3: Workflow Construction and Deterministic Runtime](03-workflow-construction-and-deterministic-runtime.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- tutorial slug: **refly-tutorial**
+- chapter focus: **Chapter 2: Architecture and Component Topology**
+- system context: **Refly Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Architecture and Component Topology`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Refly Repository](https://github.com/refly-ai/refly)
+- [README](https://github.com/refly-ai/refly/blob/main/README.md)
+- [API Guide (OpenAPI)](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/openapi.md)
+- [Webhook Guide](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/webhook.md)
+- [Contributing Guide](https://github.com/refly-ai/refly/blob/main/CONTRIBUTING.md)
+
+### Cross-Tutorial Connection Map
+
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [n8n AI Tutorial](../n8n-ai-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Architecture and Component Topology`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Architecture and Component Topology` as an operating subsystem inside **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Architecture and Component Topology` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Refly Repository](https://github.com/refly-ai/refly)
+  Why it matters: authoritative reference on `Refly Repository` (github.com).
+- [README](https://github.com/refly-ai/refly/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [API Guide (OpenAPI)](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/openapi.md)
+  Why it matters: authoritative reference on `API Guide (OpenAPI)` (github.com).
+- [Webhook Guide](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/webhook.md)
+  Why it matters: authoritative reference on `Webhook Guide` (github.com).
+- [Contributing Guide](https://github.com/refly-ai/refly/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Workflow Construction and Deterministic Runtime](03-workflow-construction-and-deterministic-runtime.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/refly-tutorial/03-workflow-construction-and-deterministic-runtime.md b/tutorials/refly-tutorial/03-workflow-construction-and-deterministic-runtime.md
index dbb67ad2..008c490e 100644
--- a/tutorials/refly-tutorial/03-workflow-construction-and-deterministic-runtime.md
+++ b/tutorials/refly-tutorial/03-workflow-construction-and-deterministic-runtime.md
@@ -7,6 +7,9 @@ parent: Refly Tutorial
 
 # Chapter 3: Workflow Construction and Deterministic Runtime
 
+Welcome to **Chapter 3: Workflow Construction and Deterministic Runtime**. In this part of **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on constructing workflows that remain stable under real operational pressure.
 
 ## Learning Goals
@@ -44,3 +47,598 @@ This chapter focuses on constructing workflows that remain stable under real ope
 You now have a practical pattern for building stable workflows and iterating safely.
 
 Next: [Chapter 4: API and Webhook Integrations](04-api-and-webhook-integrations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- tutorial slug: **refly-tutorial**
+- chapter focus: **Chapter 3: Workflow Construction and Deterministic Runtime**
+- system context: **Refly Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Workflow Construction and Deterministic Runtime`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Refly Repository](https://github.com/refly-ai/refly)
+- [README](https://github.com/refly-ai/refly/blob/main/README.md)
+- [API Guide (OpenAPI)](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/openapi.md)
+- [Webhook Guide](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/webhook.md)
+- [Contributing Guide](https://github.com/refly-ai/refly/blob/main/CONTRIBUTING.md)
+
+### Cross-Tutorial Connection Map
+
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [n8n AI Tutorial](../n8n-ai-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Workflow Construction and Deterministic Runtime`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Workflow Construction and Deterministic Runtime
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Workflow Construction and Deterministic Runtime` as an operating subsystem inside **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Workflow Construction and Deterministic Runtime` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Refly Repository](https://github.com/refly-ai/refly)
+  Why it matters: authoritative reference on `Refly Repository` (github.com).
+- [README](https://github.com/refly-ai/refly/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [API Guide (OpenAPI)](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/openapi.md)
+  Why it matters: authoritative reference on `API Guide (OpenAPI)` (github.com).
+- [Webhook Guide](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/webhook.md)
+  Why it matters: authoritative reference on `Webhook Guide` (github.com).
+- [Contributing Guide](https://github.com/refly-ai/refly/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Architecture and Component Topology](02-architecture-and-component-topology.md)
+- [Next Chapter: Chapter 4: API and Webhook Integrations](04-api-and-webhook-integrations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/refly-tutorial/04-api-and-webhook-integrations.md b/tutorials/refly-tutorial/04-api-and-webhook-integrations.md
index bc69876e..51fdf4d3 100644
--- a/tutorials/refly-tutorial/04-api-and-webhook-integrations.md
+++ b/tutorials/refly-tutorial/04-api-and-webhook-integrations.md
@@ -7,6 +7,9 @@ parent: Refly Tutorial
 
 # Chapter 4: API and Webhook Integrations
 
+Welcome to **Chapter 4: API and Webhook Integrations**. In this part of **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers the two primary operational integration surfaces for Refly workflows.
 
 ## Learning Goals
@@ -43,3 +46,598 @@ This chapter covers the two primary operational integration surfaces for Refly w
 You now have a production-style pattern for calling and monitoring Refly workflows programmatically.
 
 Next: [Chapter 5: Refly CLI and Claude Code Skill Export](05-refly-cli-and-claude-code-skill-export.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- tutorial slug: **refly-tutorial**
+- chapter focus: **Chapter 4: API and Webhook Integrations**
+- system context: **Refly Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: API and Webhook Integrations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Refly Repository](https://github.com/refly-ai/refly)
+- [README](https://github.com/refly-ai/refly/blob/main/README.md)
+- [API Guide (OpenAPI)](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/openapi.md)
+- [Webhook Guide](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/webhook.md)
+- [Contributing Guide](https://github.com/refly-ai/refly/blob/main/CONTRIBUTING.md)
+
+### Cross-Tutorial Connection Map
+
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [n8n AI Tutorial](../n8n-ai-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: API and Webhook Integrations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: API and Webhook Integrations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: API and Webhook Integrations` as an operating subsystem inside **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: API and Webhook Integrations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Refly Repository](https://github.com/refly-ai/refly)
+  Why it matters: authoritative reference on `Refly Repository` (github.com).
+- [README](https://github.com/refly-ai/refly/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [API Guide (OpenAPI)](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/openapi.md)
+  Why it matters: authoritative reference on `API Guide (OpenAPI)` (github.com).
+- [Webhook Guide](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/webhook.md)
+  Why it matters: authoritative reference on `Webhook Guide` (github.com).
+- [Contributing Guide](https://github.com/refly-ai/refly/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Workflow Construction and Deterministic Runtime](03-workflow-construction-and-deterministic-runtime.md)
+- [Next Chapter: Chapter 5: Refly CLI and Claude Code Skill Export](05-refly-cli-and-claude-code-skill-export.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/refly-tutorial/05-refly-cli-and-claude-code-skill-export.md b/tutorials/refly-tutorial/05-refly-cli-and-claude-code-skill-export.md
index a12dddac..46f6351d 100644
--- a/tutorials/refly-tutorial/05-refly-cli-and-claude-code-skill-export.md
+++ b/tutorials/refly-tutorial/05-refly-cli-and-claude-code-skill-export.md
@@ -7,6 +7,9 @@ parent: Refly Tutorial
 
 # Chapter 5: Refly CLI and Claude Code Skill Export
 
+Welcome to **Chapter 5: Refly CLI and Claude Code Skill Export**. In this part of **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains how to use the CLI for deterministic workflow operations and how Refly skills connect to Claude Code contexts.
 
 ## Learning Goals
@@ -45,3 +48,590 @@ refly workflow run <workflowId>
 You now have a deterministic CLI path for building, validating, and exporting workflow capabilities.
 
 Next: [Chapter 6: Observability, Deployment, and Operations](06-observability-deployment-and-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- tutorial slug: **refly-tutorial**
+- chapter focus: **Chapter 5: Refly CLI and Claude Code Skill Export**
+- system context: **Refly Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Refly CLI and Claude Code Skill Export`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Refly Repository](https://github.com/refly-ai/refly)
+- [README](https://github.com/refly-ai/refly/blob/main/README.md)
+- [API Guide (OpenAPI)](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/openapi.md)
+- [Webhook Guide](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/webhook.md)
+- [Contributing Guide](https://github.com/refly-ai/refly/blob/main/CONTRIBUTING.md)
+
+### Cross-Tutorial Connection Map
+
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [n8n AI Tutorial](../n8n-ai-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Refly CLI and Claude Code Skill Export`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Refly CLI and Claude Code Skill Export
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `refly`, `builder`, `workflow` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Refly CLI and Claude Code Skill Export` as an operating subsystem inside **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `install`, `init`, `login` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Refly CLI and Claude Code Skill Export` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `refly`.
+2. **Input normalization**: shape incoming data so `builder` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `workflow`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Refly Repository](https://github.com/refly-ai/refly)
+  Why it matters: authoritative reference on `Refly Repository` (github.com).
+- [README](https://github.com/refly-ai/refly/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [API Guide (OpenAPI)](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/openapi.md)
+  Why it matters: authoritative reference on `API Guide (OpenAPI)` (github.com).
+- [Webhook Guide](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/webhook.md)
+  Why it matters: authoritative reference on `Webhook Guide` (github.com).
+- [Contributing Guide](https://github.com/refly-ai/refly/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `refly` and `builder` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: API and Webhook Integrations](04-api-and-webhook-integrations.md)
+- [Next Chapter: Chapter 6: Observability, Deployment, and Operations](06-observability-deployment-and-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/refly-tutorial/06-observability-deployment-and-operations.md b/tutorials/refly-tutorial/06-observability-deployment-and-operations.md
index 3bf7aa09..cc7e397a 100644
--- a/tutorials/refly-tutorial/06-observability-deployment-and-operations.md
+++ b/tutorials/refly-tutorial/06-observability-deployment-and-operations.md
@@ -7,6 +7,9 @@ parent: Refly Tutorial
 
 # Chapter 6: Observability, Deployment, and Operations
 
+Welcome to **Chapter 6: Observability, Deployment, and Operations**. In this part of **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers operating Refly with visibility into metrics, traces, logs, and deployment surfaces.
 
 ## Learning Goals
@@ -45,3 +48,590 @@ Then verify data flow in Grafana and API checks before diagnosing workflow-level
 You now have a baseline operational model for running Refly beyond local experimentation.
 
 Next: [Chapter 7: Troubleshooting, Safety, and Cost Controls](07-troubleshooting-safety-and-cost-controls.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- tutorial slug: **refly-tutorial**
+- chapter focus: **Chapter 6: Observability, Deployment, and Operations**
+- system context: **Refly Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Observability, Deployment, and Operations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Refly Repository](https://github.com/refly-ai/refly)
+- [README](https://github.com/refly-ai/refly/blob/main/README.md)
+- [API Guide (OpenAPI)](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/openapi.md)
+- [Webhook Guide](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/webhook.md)
+- [Contributing Guide](https://github.com/refly-ai/refly/blob/main/CONTRIBUTING.md)
+
+### Cross-Tutorial Connection Map
+
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [n8n AI Tutorial](../n8n-ai-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Observability, Deployment, and Operations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Observability, Deployment, and Operations
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `docker`, `deploy`, `trace` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Observability, Deployment, and Operations` as an operating subsystem inside **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `compose` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Observability, Deployment, and Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `docker`.
+2. **Input normalization**: shape incoming data so `deploy` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `trace`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Refly Repository](https://github.com/refly-ai/refly)
+  Why it matters: authoritative reference on `Refly Repository` (github.com).
+- [README](https://github.com/refly-ai/refly/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [API Guide (OpenAPI)](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/openapi.md)
+  Why it matters: authoritative reference on `API Guide (OpenAPI)` (github.com).
+- [Webhook Guide](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/webhook.md)
+  Why it matters: authoritative reference on `Webhook Guide` (github.com).
+- [Contributing Guide](https://github.com/refly-ai/refly/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `docker` and `deploy` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Refly CLI and Claude Code Skill Export](05-refly-cli-and-claude-code-skill-export.md)
+- [Next Chapter: Chapter 7: Troubleshooting, Safety, and Cost Controls](07-troubleshooting-safety-and-cost-controls.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/refly-tutorial/07-troubleshooting-safety-and-cost-controls.md b/tutorials/refly-tutorial/07-troubleshooting-safety-and-cost-controls.md
index 6d131d2f..55443b3c 100644
--- a/tutorials/refly-tutorial/07-troubleshooting-safety-and-cost-controls.md
+++ b/tutorials/refly-tutorial/07-troubleshooting-safety-and-cost-controls.md
@@ -7,6 +7,9 @@ parent: Refly Tutorial
 
 # Chapter 7: Troubleshooting, Safety, and Cost Controls
 
+Welcome to **Chapter 7: Troubleshooting, Safety, and Cost Controls**. In this part of **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter provides pragmatic recovery and guardrail practices for production usage.
 
 ## Learning Goals
@@ -43,3 +46,598 @@ This chapter provides pragmatic recovery and guardrail practices for production
 You now have a practical troubleshooting and safety playbook for Refly operations.
 
 Next: [Chapter 8: Contribution Workflow and Governance](08-contribution-workflow-and-governance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- tutorial slug: **refly-tutorial**
+- chapter focus: **Chapter 7: Troubleshooting, Safety, and Cost Controls**
+- system context: **Refly Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Troubleshooting, Safety, and Cost Controls`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Refly Repository](https://github.com/refly-ai/refly)
+- [README](https://github.com/refly-ai/refly/blob/main/README.md)
+- [API Guide (OpenAPI)](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/openapi.md)
+- [Webhook Guide](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/webhook.md)
+- [Contributing Guide](https://github.com/refly-ai/refly/blob/main/CONTRIBUTING.md)
+
+### Cross-Tutorial Connection Map
+
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [n8n AI Tutorial](../n8n-ai-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Troubleshooting, Safety, and Cost Controls`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Troubleshooting, Safety, and Cost Controls
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Troubleshooting, Safety, and Cost Controls` as an operating subsystem inside **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Troubleshooting, Safety, and Cost Controls` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Refly Repository](https://github.com/refly-ai/refly)
+  Why it matters: authoritative reference on `Refly Repository` (github.com).
+- [README](https://github.com/refly-ai/refly/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [API Guide (OpenAPI)](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/openapi.md)
+  Why it matters: authoritative reference on `API Guide (OpenAPI)` (github.com).
+- [Webhook Guide](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/webhook.md)
+  Why it matters: authoritative reference on `Webhook Guide` (github.com).
+- [Contributing Guide](https://github.com/refly-ai/refly/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Observability, Deployment, and Operations](06-observability-deployment-and-operations.md)
+- [Next Chapter: Chapter 8: Contribution Workflow and Governance](08-contribution-workflow-and-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/refly-tutorial/08-contribution-workflow-and-governance.md b/tutorials/refly-tutorial/08-contribution-workflow-and-governance.md
index 29d615b5..ab62f75b 100644
--- a/tutorials/refly-tutorial/08-contribution-workflow-and-governance.md
+++ b/tutorials/refly-tutorial/08-contribution-workflow-and-governance.md
@@ -7,6 +7,9 @@ parent: Refly Tutorial
 
 # Chapter 8: Contribution Workflow and Governance
 
+Welcome to **Chapter 8: Contribution Workflow and Governance**. In this part of **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers contribution expectations and governance norms for sustainable ecosystem growth.
 
 ## Learning Goals
@@ -45,3 +48,585 @@ Next steps:
 - run one full workflow via API and webhook to compare behavior
 - export and test one skill in your Claude Code environment
 - contribute one focused improvement with docs and validation notes
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- tutorial slug: **refly-tutorial**
+- chapter focus: **Chapter 8: Contribution Workflow and Governance**
+- system context: **Refly Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Contribution Workflow and Governance`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Refly Repository](https://github.com/refly-ai/refly)
+- [README](https://github.com/refly-ai/refly/blob/main/README.md)
+- [API Guide (OpenAPI)](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/openapi.md)
+- [Webhook Guide](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/webhook.md)
+- [Contributing Guide](https://github.com/refly-ai/refly/blob/main/CONTRIBUTING.md)
+
+### Cross-Tutorial Connection Map
+
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [n8n AI Tutorial](../n8n-ai-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Contribution Workflow and Governance`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Contribution Workflow and Governance` as an operating subsystem inside **Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Contribution Workflow and Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Refly Repository](https://github.com/refly-ai/refly)
+  Why it matters: authoritative reference on `Refly Repository` (github.com).
+- [README](https://github.com/refly-ai/refly/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [API Guide (OpenAPI)](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/openapi.md)
+  Why it matters: authoritative reference on `API Guide (OpenAPI)` (github.com).
+- [Webhook Guide](https://github.com/refly-ai/refly/blob/main/docs/en/guide/api/webhook.md)
+  Why it matters: authoritative reference on `Webhook Guide` (github.com).
+- [Contributing Guide](https://github.com/refly-ai/refly/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Troubleshooting, Safety, and Cost Controls](07-troubleshooting-safety-and-cost-controls.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/roo-code-tutorial/01-getting-started.md b/tutorials/roo-code-tutorial/01-getting-started.md
index fa6cc898..d80ce33e 100644
--- a/tutorials/roo-code-tutorial/01-getting-started.md
+++ b/tutorials/roo-code-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Roo Code Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Roo Code Tutorial: Run an AI Dev Team in Your Editor**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter establishes a stable Roo Code baseline in a VS Code-compatible workflow.
 
 ## Objectives
@@ -126,3 +129,515 @@ You now have Roo Code running with:
 - initial safety policy in place
 
 Next: [Chapter 2: Modes and Task Design](02-modes-and-task-design.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- tutorial slug: **roo-code-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Roo Code Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Roo Code README](https://github.com/RooCodeInc/Roo-Code/blob/main/README.md)
+- [Roo Code Docs](https://docs.roocode.com/)
+- [Using Modes docs page](https://docs.roocode.com/basic-usage/using-modes)
+- [Roo Code Releases](https://github.com/RooCodeInc/Roo-Code/releases)
+
+### Cross-Tutorial Connection Map
+
+- [Cline Tutorial](../cline-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `pnpm`, `install`, `vsix` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Roo Code Tutorial: Run an AI Dev Team in Your Editor**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Code`, `clone`, `https` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `pnpm`.
+2. **Input normalization**: shape incoming data so `install` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `vsix`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Roo Code README](https://github.com/RooCodeInc/Roo-Code/blob/main/README.md)
+  Why it matters: authoritative reference on `Roo Code README` (github.com).
+- [Roo Code Docs](https://docs.roocode.com/)
+  Why it matters: authoritative reference on `Roo Code Docs` (docs.roocode.com).
+- [Using Modes docs page](https://docs.roocode.com/basic-usage/using-modes)
+  Why it matters: authoritative reference on `Using Modes docs page` (docs.roocode.com).
+- [Roo Code Releases](https://github.com/RooCodeInc/Roo-Code/releases)
+  Why it matters: authoritative reference on `Roo Code Releases` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `pnpm` and `install` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Modes and Task Design](02-modes-and-task-design.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/roo-code-tutorial/02-modes-and-task-design.md b/tutorials/roo-code-tutorial/02-modes-and-task-design.md
index 67e602d1..b23e0461 100644
--- a/tutorials/roo-code-tutorial/02-modes-and-task-design.md
+++ b/tutorials/roo-code-tutorial/02-modes-and-task-design.md
@@ -7,6 +7,9 @@ parent: Roo Code Tutorial
 
 # Chapter 2: Modes and Task Design
 
+Welcome to **Chapter 2: Modes and Task Design**. In this part of **Roo Code Tutorial: Run an AI Dev Team in Your Editor**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Roo Code's mode system is its core quality-control mechanism. This chapter shows how to choose and sequence modes deliberately.
 
 ## Mode Landscape
@@ -105,3 +108,528 @@ You now have a mode-driven execution framework that supports:
 - reusable custom-mode behavior for teams
 
 Next: [Chapter 3: File and Command Operations](03-file-and-command-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- tutorial slug: **roo-code-tutorial**
+- chapter focus: **Chapter 2: Modes and Task Design**
+- system context: **Roo Code Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Modes and Task Design`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Roo Code README](https://github.com/RooCodeInc/Roo-Code/blob/main/README.md)
+- [Roo Code Docs](https://docs.roocode.com/)
+- [Using Modes docs page](https://docs.roocode.com/basic-usage/using-modes)
+- [Roo Code Releases](https://github.com/RooCodeInc/Roo-Code/releases)
+
+### Cross-Tutorial Connection Map
+
+- [Cline Tutorial](../cline-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Modes and Task Design`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Modes and Task Design
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Mode`, `flowchart`, `Architect` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Modes and Task Design` as an operating subsystem inside **Roo Code Tutorial: Run an AI Dev Team in Your Editor**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Plan`, `Approved`, `Code` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Modes and Task Design` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Mode`.
+2. **Input normalization**: shape incoming data so `flowchart` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Architect`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Roo Code README](https://github.com/RooCodeInc/Roo-Code/blob/main/README.md)
+  Why it matters: authoritative reference on `Roo Code README` (github.com).
+- [Roo Code Docs](https://docs.roocode.com/)
+  Why it matters: authoritative reference on `Roo Code Docs` (docs.roocode.com).
+- [Using Modes docs page](https://docs.roocode.com/basic-usage/using-modes)
+  Why it matters: authoritative reference on `Using Modes docs page` (docs.roocode.com).
+- [Roo Code Releases](https://github.com/RooCodeInc/Roo-Code/releases)
+  Why it matters: authoritative reference on `Roo Code Releases` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Mode` and `flowchart` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: File and Command Operations](03-file-and-command-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/roo-code-tutorial/03-file-and-command-operations.md b/tutorials/roo-code-tutorial/03-file-and-command-operations.md
index be542d61..edd4f01c 100644
--- a/tutorials/roo-code-tutorial/03-file-and-command-operations.md
+++ b/tutorials/roo-code-tutorial/03-file-and-command-operations.md
@@ -7,6 +7,9 @@ parent: Roo Code Tutorial
 
 # Chapter 3: File and Command Operations
 
+Welcome to **Chapter 3: File and Command Operations**. In this part of **Roo Code Tutorial: Run an AI Dev Team in Your Editor**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers the most common and risky Roo Code actions: patching files and executing commands.
 
 ## The Controlled Loop
@@ -95,3 +98,540 @@ You now have a governance model for Roo edit/command loops:
 - audit-friendly evidence capture
 
 Next: [Chapter 4: Context and Indexing](04-context-and-indexing.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- tutorial slug: **roo-code-tutorial**
+- chapter focus: **Chapter 3: File and Command Operations**
+- system context: **Roo Code Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: File and Command Operations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Roo Code README](https://github.com/RooCodeInc/Roo-Code/blob/main/README.md)
+- [Roo Code Docs](https://docs.roocode.com/)
+- [Using Modes docs page](https://docs.roocode.com/basic-usage/using-modes)
+- [Roo Code Releases](https://github.com/RooCodeInc/Roo-Code/releases)
+
+### Cross-Tutorial Connection Map
+
+- [Cline Tutorial](../cline-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: File and Command Operations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: File and Command Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `pnpm`, `test`, `lint` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: File and Command Operations` as an operating subsystem inside **Roo Code Tutorial: Run an AI Dev Team in Your Editor**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `build`, `target`, `module` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: File and Command Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `pnpm`.
+2. **Input normalization**: shape incoming data so `test` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `lint`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Roo Code README](https://github.com/RooCodeInc/Roo-Code/blob/main/README.md)
+  Why it matters: authoritative reference on `Roo Code README` (github.com).
+- [Roo Code Docs](https://docs.roocode.com/)
+  Why it matters: authoritative reference on `Roo Code Docs` (docs.roocode.com).
+- [Using Modes docs page](https://docs.roocode.com/basic-usage/using-modes)
+  Why it matters: authoritative reference on `Using Modes docs page` (docs.roocode.com).
+- [Roo Code Releases](https://github.com/RooCodeInc/Roo-Code/releases)
+  Why it matters: authoritative reference on `Roo Code Releases` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `pnpm` and `test` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Modes and Task Design](02-modes-and-task-design.md)
+- [Next Chapter: Chapter 4: Context and Indexing](04-context-and-indexing.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/roo-code-tutorial/04-context-and-indexing.md b/tutorials/roo-code-tutorial/04-context-and-indexing.md
index 3d54f844..e063253d 100644
--- a/tutorials/roo-code-tutorial/04-context-and-indexing.md
+++ b/tutorials/roo-code-tutorial/04-context-and-indexing.md
@@ -7,6 +7,9 @@ parent: Roo Code Tutorial
 
 # Chapter 4: Context and Indexing
 
+Welcome to **Chapter 4: Context and Indexing**. In this part of **Roo Code Tutorial: Run an AI Dev Team in Your Editor**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In large repositories, quality depends on context precision. This chapter covers how to manage context and indexing strategy in Roo workflows.
 
 ## Core Principle
@@ -97,3 +100,540 @@ You now have a context/indexing model for large repos:
 - maintain continuity across mode transitions
 
 Next: [Chapter 5: Checkpoints and Recovery](05-checkpoints-and-recovery.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- tutorial slug: **roo-code-tutorial**
+- chapter focus: **Chapter 4: Context and Indexing**
+- system context: **Roo Code Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Context and Indexing`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Roo Code README](https://github.com/RooCodeInc/Roo-Code/blob/main/README.md)
+- [Roo Code Docs](https://docs.roocode.com/)
+- [Using Modes docs page](https://docs.roocode.com/basic-usage/using-modes)
+- [Roo Code Releases](https://github.com/RooCodeInc/Roo-Code/releases)
+
+### Cross-Tutorial Connection Map
+
+- [Cline Tutorial](../cline-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Context and Indexing`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Context and Indexing
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Context`, `flowchart`, `Task` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Context and Indexing` as an operating subsystem inside **Roo Code Tutorial: Run an AI Dev Team in Your Editor**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Goal`, `Index`, `Search` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Context and Indexing` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Context`.
+2. **Input normalization**: shape incoming data so `flowchart` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Task`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Roo Code README](https://github.com/RooCodeInc/Roo-Code/blob/main/README.md)
+  Why it matters: authoritative reference on `Roo Code README` (github.com).
+- [Roo Code Docs](https://docs.roocode.com/)
+  Why it matters: authoritative reference on `Roo Code Docs` (docs.roocode.com).
+- [Using Modes docs page](https://docs.roocode.com/basic-usage/using-modes)
+  Why it matters: authoritative reference on `Using Modes docs page` (docs.roocode.com).
+- [Roo Code Releases](https://github.com/RooCodeInc/Roo-Code/releases)
+  Why it matters: authoritative reference on `Roo Code Releases` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Context` and `flowchart` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: File and Command Operations](03-file-and-command-operations.md)
+- [Next Chapter: Chapter 5: Checkpoints and Recovery](05-checkpoints-and-recovery.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/roo-code-tutorial/05-checkpoints-and-recovery.md b/tutorials/roo-code-tutorial/05-checkpoints-and-recovery.md
index 89f5d37d..8aba3a2a 100644
--- a/tutorials/roo-code-tutorial/05-checkpoints-and-recovery.md
+++ b/tutorials/roo-code-tutorial/05-checkpoints-and-recovery.md
@@ -7,6 +7,9 @@ parent: Roo Code Tutorial
 
 # Chapter 5: Checkpoints and Recovery
 
+Welcome to **Chapter 5: Checkpoints and Recovery**. In this part of **Roo Code Tutorial: Run an AI Dev Team in Your Editor**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Checkpoints are essential for safe experimentation. This chapter explains when to checkpoint, how to compare states, and how to recover cleanly.
 
 ## Why Checkpoints Matter
@@ -83,3 +86,552 @@ You now have a checkpoint-driven reliability model:
 - cleaner recovery during high-velocity iteration
 
 Next: [Chapter 6: MCP and Tool Extensions](06-mcp-and-tool-extensions.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- tutorial slug: **roo-code-tutorial**
+- chapter focus: **Chapter 5: Checkpoints and Recovery**
+- system context: **Roo Code Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Checkpoints and Recovery`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Roo Code README](https://github.com/RooCodeInc/Roo-Code/blob/main/README.md)
+- [Roo Code Docs](https://docs.roocode.com/)
+- [Using Modes docs page](https://docs.roocode.com/basic-usage/using-modes)
+- [Roo Code Releases](https://github.com/RooCodeInc/Roo-Code/releases)
+
+### Cross-Tutorial Connection Map
+
+- [Cline Tutorial](../cline-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Checkpoints and Recovery`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Checkpoints and Recovery
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Checkpoint`, `flowchart`, `Create` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Checkpoints and Recovery` as an operating subsystem inside **Roo Code Tutorial: Run an AI Dev Team in Your Editor**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Apply`, `Candidate`, `Patch` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Checkpoints and Recovery` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Checkpoint`.
+2. **Input normalization**: shape incoming data so `flowchart` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Create`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Roo Code README](https://github.com/RooCodeInc/Roo-Code/blob/main/README.md)
+  Why it matters: authoritative reference on `Roo Code README` (github.com).
+- [Roo Code Docs](https://docs.roocode.com/)
+  Why it matters: authoritative reference on `Roo Code Docs` (docs.roocode.com).
+- [Using Modes docs page](https://docs.roocode.com/basic-usage/using-modes)
+  Why it matters: authoritative reference on `Using Modes docs page` (docs.roocode.com).
+- [Roo Code Releases](https://github.com/RooCodeInc/Roo-Code/releases)
+  Why it matters: authoritative reference on `Roo Code Releases` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Checkpoint` and `flowchart` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Context and Indexing](04-context-and-indexing.md)
+- [Next Chapter: Chapter 6: MCP and Tool Extensions](06-mcp-and-tool-extensions.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/roo-code-tutorial/06-mcp-and-tool-extensions.md b/tutorials/roo-code-tutorial/06-mcp-and-tool-extensions.md
index 3f2e70c5..c223a536 100644
--- a/tutorials/roo-code-tutorial/06-mcp-and-tool-extensions.md
+++ b/tutorials/roo-code-tutorial/06-mcp-and-tool-extensions.md
@@ -7,6 +7,9 @@ parent: Roo Code Tutorial
 
 # Chapter 6: MCP and Tool Extensions
 
+Welcome to **Chapter 6: MCP and Tool Extensions**. In this part of **Roo Code Tutorial: Run an AI Dev Team in Your Editor**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Roo Code becomes a platform interface when connected to external tools. This chapter defines a safe rollout model for MCP and custom tool extensions.
 
 ## Typical Integration Domains
@@ -81,3 +84,552 @@ You now have a practical extension strategy for Roo Code:
 - secure credential and audit boundaries
 
 Next: [Chapter 7: Profiles and Team Standards](07-profiles-and-team-standards.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- tutorial slug: **roo-code-tutorial**
+- chapter focus: **Chapter 6: MCP and Tool Extensions**
+- system context: **Roo Code Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: MCP and Tool Extensions`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Roo Code README](https://github.com/RooCodeInc/Roo-Code/blob/main/README.md)
+- [Roo Code Docs](https://docs.roocode.com/)
+- [Using Modes docs page](https://docs.roocode.com/basic-usage/using-modes)
+- [Roo Code Releases](https://github.com/RooCodeInc/Roo-Code/releases)
+
+### Cross-Tutorial Connection Map
+
+- [Cline Tutorial](../cline-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: MCP and Tool Extensions`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: MCP and Tool Extensions
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Tool`, `flowchart`, `Task` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: MCP and Tool Extensions` as an operating subsystem inside **Roo Code Tutorial: Run an AI Dev Team in Your Editor**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Client`, `Layer`, `Docs` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: MCP and Tool Extensions` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Tool`.
+2. **Input normalization**: shape incoming data so `flowchart` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Task`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Roo Code README](https://github.com/RooCodeInc/Roo-Code/blob/main/README.md)
+  Why it matters: authoritative reference on `Roo Code README` (github.com).
+- [Roo Code Docs](https://docs.roocode.com/)
+  Why it matters: authoritative reference on `Roo Code Docs` (docs.roocode.com).
+- [Using Modes docs page](https://docs.roocode.com/basic-usage/using-modes)
+  Why it matters: authoritative reference on `Using Modes docs page` (docs.roocode.com).
+- [Roo Code Releases](https://github.com/RooCodeInc/Roo-Code/releases)
+  Why it matters: authoritative reference on `Roo Code Releases` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Tool` and `flowchart` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Checkpoints and Recovery](05-checkpoints-and-recovery.md)
+- [Next Chapter: Chapter 7: Profiles and Team Standards](07-profiles-and-team-standards.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/roo-code-tutorial/07-profiles-and-team-standards.md b/tutorials/roo-code-tutorial/07-profiles-and-team-standards.md
index 36cb9b39..b7802835 100644
--- a/tutorials/roo-code-tutorial/07-profiles-and-team-standards.md
+++ b/tutorials/roo-code-tutorial/07-profiles-and-team-standards.md
@@ -7,6 +7,9 @@ parent: Roo Code Tutorial
 
 # Chapter 7: Profiles and Team Standards
 
+Welcome to **Chapter 7: Profiles and Team Standards**. In this part of **Roo Code Tutorial: Run an AI Dev Team in Your Editor**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Profiles are the mechanism for making Roo behavior consistent across individuals and repositories.
 
 ## Why Profiles Matter
@@ -76,3 +79,560 @@ You now have a profile-driven scaling model for Roo Code:
 - governance against policy drift
 
 Next: [Chapter 8: Enterprise Operations](08-enterprise-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- tutorial slug: **roo-code-tutorial**
+- chapter focus: **Chapter 7: Profiles and Team Standards**
+- system context: **Roo Code Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Profiles and Team Standards`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Roo Code README](https://github.com/RooCodeInc/Roo-Code/blob/main/README.md)
+- [Roo Code Docs](https://docs.roocode.com/)
+- [Using Modes docs page](https://docs.roocode.com/basic-usage/using-modes)
+- [Roo Code Releases](https://github.com/RooCodeInc/Roo-Code/releases)
+
+### Cross-Tutorial Connection Map
+
+- [Cline Tutorial](../cline-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Profiles and Team Standards`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Profiles and Team Standards
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Profiles and Team Standards` as an operating subsystem inside **Roo Code Tutorial: Run an AI Dev Team in Your Editor**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Profiles and Team Standards` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Roo Code README](https://github.com/RooCodeInc/Roo-Code/blob/main/README.md)
+  Why it matters: authoritative reference on `Roo Code README` (github.com).
+- [Roo Code Docs](https://docs.roocode.com/)
+  Why it matters: authoritative reference on `Roo Code Docs` (docs.roocode.com).
+- [Using Modes docs page](https://docs.roocode.com/basic-usage/using-modes)
+  Why it matters: authoritative reference on `Using Modes docs page` (docs.roocode.com).
+- [Roo Code Releases](https://github.com/RooCodeInc/Roo-Code/releases)
+  Why it matters: authoritative reference on `Roo Code Releases` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: MCP and Tool Extensions](06-mcp-and-tool-extensions.md)
+- [Next Chapter: Chapter 8: Enterprise Operations](08-enterprise-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/roo-code-tutorial/08-enterprise-operations.md b/tutorials/roo-code-tutorial/08-enterprise-operations.md
index 468b4c31..1ba1df50 100644
--- a/tutorials/roo-code-tutorial/08-enterprise-operations.md
+++ b/tutorials/roo-code-tutorial/08-enterprise-operations.md
@@ -7,6 +7,9 @@ parent: Roo Code Tutorial
 
 # Chapter 8: Enterprise Operations
 
+Welcome to **Chapter 8: Enterprise Operations**. In this part of **Roo Code Tutorial: Run an AI Dev Team in Your Editor**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter defines a practical operations model for running Roo Code at organizational scale.
 
 ## Production Readiness Criteria
@@ -102,3 +105,539 @@ Related:
 - [Continue Tutorial](../continue-tutorial/)
 - [OpenHands Tutorial](../openhands-tutorial/)
 - [MCP Servers Tutorial](../mcp-servers-tutorial/)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- tutorial slug: **roo-code-tutorial**
+- chapter focus: **Chapter 8: Enterprise Operations**
+- system context: **Roo Code Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Enterprise Operations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Roo Code README](https://github.com/RooCodeInc/Roo-Code/blob/main/README.md)
+- [Roo Code Docs](https://docs.roocode.com/)
+- [Using Modes docs page](https://docs.roocode.com/basic-usage/using-modes)
+- [Roo Code Releases](https://github.com/RooCodeInc/Roo-Code/releases)
+
+### Cross-Tutorial Connection Map
+
+- [Cline Tutorial](../cline-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Enterprise Operations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Enterprise Operations
+
+- tutorial context: **Roo Code Tutorial: Run an AI Dev Team in Your Editor**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Policy`, `flowchart`, `User` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Enterprise Operations` as an operating subsystem inside **Roo Code Tutorial: Run an AI Dev Team in Your Editor**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Tasks`, `Identity`, `Controls` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Enterprise Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Policy`.
+2. **Input normalization**: shape incoming data so `flowchart` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `User`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Roo Code README](https://github.com/RooCodeInc/Roo-Code/blob/main/README.md)
+  Why it matters: authoritative reference on `Roo Code README` (github.com).
+- [Roo Code Docs](https://docs.roocode.com/)
+  Why it matters: authoritative reference on `Roo Code Docs` (docs.roocode.com).
+- [Using Modes docs page](https://docs.roocode.com/basic-usage/using-modes)
+  Why it matters: authoritative reference on `Using Modes docs page` (docs.roocode.com).
+- [Roo Code Releases](https://github.com/RooCodeInc/Roo-Code/releases)
+  Why it matters: authoritative reference on `Roo Code Releases` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Policy` and `flowchart` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Profiles and Team Standards](07-profiles-and-team-standards.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/semantic-kernel-tutorial/01-getting-started.md b/tutorials/semantic-kernel-tutorial/01-getting-started.md
index dcd58351..ab42cf22 100644
--- a/tutorials/semantic-kernel-tutorial/01-getting-started.md
+++ b/tutorials/semantic-kernel-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: Semantic Kernel Tutorial
 
 # Chapter 1: Getting Started with Semantic Kernel
 
+Welcome to **Chapter 1: Getting Started with Semantic Kernel**. In this part of **Semantic Kernel Tutorial: Microsoft's AI Orchestration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Install Semantic Kernel, wire up your first AI service, and run a simple plugin-powered prompt in minutes.
 
 ## What is Semantic Kernel?
@@ -428,3 +431,50 @@ In **[Chapter 2: Plugins & Functions](02-plugins.md)**, you will learn how to bu
 ---
 
 *Built with insights from the [Semantic Kernel](https://github.com/microsoft/semantic-kernel) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `kernel`, `OpenAI`, `Kernel` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Semantic Kernel` as an operating subsystem inside **Semantic Kernel Tutorial: Microsoft's AI Orchestration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Microsoft`, `builder`, `semantic` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Semantic Kernel` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `kernel`.
+2. **Input normalization**: shape incoming data so `OpenAI` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Kernel`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/microsoft/semantic-kernel)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `kernel` and `OpenAI` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Plugins & Functions](02-plugins.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/semantic-kernel-tutorial/02-plugins.md b/tutorials/semantic-kernel-tutorial/02-plugins.md
index 4d1f96ae..eae85a42 100644
--- a/tutorials/semantic-kernel-tutorial/02-plugins.md
+++ b/tutorials/semantic-kernel-tutorial/02-plugins.md
@@ -8,6 +8,9 @@ parent: Semantic Kernel Tutorial
 
 # Chapter 2: Plugins & Functions
 
+Welcome to **Chapter 2: Plugins & Functions**. In this part of **Semantic Kernel Tutorial: Microsoft's AI Orchestration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Build native and semantic functions, package them as plugins, and compose them for reusable AI capabilities.
 
 ## What Are Plugins?
@@ -729,3 +732,51 @@ In **[Chapter 3: Prompt Engineering](03-prompts.md)**, you will learn how to des
 ---
 
 *Built with insights from the [Semantic Kernel](https://github.com/microsoft/semantic-kernel) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `kernel`, `text`, `words` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Plugins & Functions` as an operating subsystem inside **Semantic Kernel Tutorial: Microsoft's AI Orchestration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `prompt`, `description`, `result` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Plugins & Functions` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `kernel`.
+2. **Input normalization**: shape incoming data so `text` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `words`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/microsoft/semantic-kernel)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `kernel` and `text` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Semantic Kernel](01-getting-started.md)
+- [Next Chapter: Chapter 3: Prompt Engineering](03-prompts.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/semantic-kernel-tutorial/03-prompts.md b/tutorials/semantic-kernel-tutorial/03-prompts.md
index 6a163318..63ca89a5 100644
--- a/tutorials/semantic-kernel-tutorial/03-prompts.md
+++ b/tutorials/semantic-kernel-tutorial/03-prompts.md
@@ -8,6 +8,9 @@ parent: Semantic Kernel Tutorial
 
 # Chapter 3: Prompt Engineering
 
+Welcome to **Chapter 3: Prompt Engineering**. In this part of **Semantic Kernel Tutorial: Microsoft's AI Orchestration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Design resilient prompt templates with variables, few-shot examples, safety rails, and output controls.
 
 ## Why Prompt Engineering Matters
@@ -765,3 +768,51 @@ In **[Chapter 4: AI Services & Connectors](04-services.md)**, you will learn how
 ---
 
 *Built with insights from the [Semantic Kernel](https://github.com/microsoft/semantic-kernel) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `question`, `context`, `description` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Prompt Engineering` as an operating subsystem inside **Semantic Kernel Tutorial: Microsoft's AI Orchestration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `name`, `kernel`, `text` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Prompt Engineering` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `question`.
+2. **Input normalization**: shape incoming data so `context` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `description`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/microsoft/semantic-kernel)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `question` and `context` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Plugins & Functions](02-plugins.md)
+- [Next Chapter: Chapter 4: AI Services & Connectors](04-services.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/semantic-kernel-tutorial/04-services.md b/tutorials/semantic-kernel-tutorial/04-services.md
index 7122f208..6c269f27 100644
--- a/tutorials/semantic-kernel-tutorial/04-services.md
+++ b/tutorials/semantic-kernel-tutorial/04-services.md
@@ -8,6 +8,9 @@ parent: Semantic Kernel Tutorial
 
 # Chapter 4: AI Services & Connectors
 
+Welcome to **Chapter 4: AI Services & Connectors**. In this part of **Semantic Kernel Tutorial: Microsoft's AI Orchestration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Connect OpenAI, Azure OpenAI, Hugging Face, and local models with retries, fallbacks, and routing.
 
 ## The Service Layer
@@ -716,3 +719,51 @@ In **[Chapter 5: Memory & Embeddings](05-memory.md)**, you will learn how to add
 ---
 
 *Built with insights from the [Semantic Kernel](https://github.com/microsoft/semantic-kernel) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `kernel`, `service_id`, `self` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: AI Services & Connectors` as an operating subsystem inside **Semantic Kernel Tutorial: Microsoft's AI Orchestration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `chat`, `requirements`, `openai` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: AI Services & Connectors` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `kernel`.
+2. **Input normalization**: shape incoming data so `service_id` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `self`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/microsoft/semantic-kernel)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `kernel` and `service_id` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Prompt Engineering](03-prompts.md)
+- [Next Chapter: Chapter 5: Memory & Embeddings](05-memory.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/semantic-kernel-tutorial/05-memory.md b/tutorials/semantic-kernel-tutorial/05-memory.md
index e25f20e5..d6f919f2 100644
--- a/tutorials/semantic-kernel-tutorial/05-memory.md
+++ b/tutorials/semantic-kernel-tutorial/05-memory.md
@@ -8,6 +8,9 @@ parent: Semantic Kernel Tutorial
 
 # Chapter 5: Memory & Embeddings
 
+Welcome to **Chapter 5: Memory & Embeddings**. In this part of **Semantic Kernel Tutorial: Microsoft's AI Orchestration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Add semantic memory with vector stores, embeddings, and grounded retrieval to build knowledge-aware applications.
 
 ## What is Semantic Memory?
@@ -783,3 +786,51 @@ In **[Chapter 6: Planners](06-planners.md)**, you will learn how to use AI-power
 ---
 
 *Built with insights from the [Semantic Kernel](https://github.com/microsoft/semantic-kernel) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `memory`, `text`, `self` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Memory & Embeddings` as an operating subsystem inside **Semantic Kernel Tutorial: Microsoft's AI Orchestration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `collection`, `chunks`, `source` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Memory & Embeddings` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `memory`.
+2. **Input normalization**: shape incoming data so `text` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `self`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/microsoft/semantic-kernel)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `memory` and `text` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: AI Services & Connectors](04-services.md)
+- [Next Chapter: Chapter 6: Planners](06-planners.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/semantic-kernel-tutorial/06-planners.md b/tutorials/semantic-kernel-tutorial/06-planners.md
index 982c9fea..0fd64383 100644
--- a/tutorials/semantic-kernel-tutorial/06-planners.md
+++ b/tutorials/semantic-kernel-tutorial/06-planners.md
@@ -8,6 +8,9 @@ parent: Semantic Kernel Tutorial
 
 # Chapter 6: Planners
 
+Welcome to **Chapter 6: Planners**. In this part of **Semantic Kernel Tutorial: Microsoft's AI Orchestration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Use planners to break down goals into executable steps using your plugins and AI services.
 
 ## What Are Planners?
@@ -706,3 +709,51 @@ In **[Chapter 7: Agents](07-agents.md)**, you will learn how to combine plugins,
 ---
 
 *Built with insights from the [Semantic Kernel](https://github.com/microsoft/semantic-kernel) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `step`, `plan`, `kernel` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Planners` as an operating subsystem inside **Semantic Kernel Tutorial: Microsoft's AI Orchestration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `print`, `Plan`, `search` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Planners` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `step`.
+2. **Input normalization**: shape incoming data so `plan` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `kernel`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/microsoft/semantic-kernel)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `step` and `plan` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Memory & Embeddings](05-memory.md)
+- [Next Chapter: Chapter 7: Agents & Tool Use](07-agents.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/semantic-kernel-tutorial/07-agents.md b/tutorials/semantic-kernel-tutorial/07-agents.md
index dde41cc6..1ed62ba0 100644
--- a/tutorials/semantic-kernel-tutorial/07-agents.md
+++ b/tutorials/semantic-kernel-tutorial/07-agents.md
@@ -8,6 +8,9 @@ parent: Semantic Kernel Tutorial
 
 # Chapter 7: Agents & Tool Use
 
+Welcome to **Chapter 7: Agents & Tool Use**. In this part of **Semantic Kernel Tutorial: Microsoft's AI Orchestration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Combine plugins, memory, and planners to build autonomous, tool-using agents with governance and safety controls.
 
 ## What Are Agents?
@@ -774,3 +777,51 @@ In **[Chapter 8: Production Deployment](08-production.md)**, you will learn how
 ---
 
 *Built with insights from the [Semantic Kernel](https://github.com/microsoft/semantic-kernel) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `kernel`, `memory` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Agents & Tool Use` as an operating subsystem inside **Semantic Kernel Tutorial: Microsoft's AI Orchestration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `history`, `agent`, `context` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Agents & Tool Use` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `kernel` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `memory`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/microsoft/semantic-kernel)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `kernel` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Planners](06-planners.md)
+- [Next Chapter: Chapter 8: Production Deployment & Operations](08-production.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/semantic-kernel-tutorial/08-production.md b/tutorials/semantic-kernel-tutorial/08-production.md
index 7e743119..0597e3c0 100644
--- a/tutorials/semantic-kernel-tutorial/08-production.md
+++ b/tutorials/semantic-kernel-tutorial/08-production.md
@@ -8,6 +8,9 @@ parent: Semantic Kernel Tutorial
 
 # Chapter 8: Production Deployment & Operations
 
+Welcome to **Chapter 8: Production Deployment & Operations**. In this part of **Semantic Kernel Tutorial: Microsoft's AI Orchestration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy Semantic Kernel-based apps with scalable architecture, Kubernetes manifests, security hardening, and observability.
 
 ## Production Architecture
@@ -1029,3 +1032,50 @@ Deploying Semantic Kernel applications to production requires attention to every
 ---
 
 *Built with insights from the [Semantic Kernel](https://github.com/microsoft/semantic-kernel) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `kernel`, `name` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment & Operations` as an operating subsystem inside **Semantic Kernel Tutorial: Microsoft's AI Orchestration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `request`, `classDef`, `fill` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment & Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `kernel` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `name`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/microsoft/semantic-kernel)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `kernel` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Agents & Tool Use](07-agents.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/serena-tutorial/01-getting-started.md b/tutorials/serena-tutorial/01-getting-started.md
index fb2ab105..cb96b193 100644
--- a/tutorials/serena-tutorial/01-getting-started.md
+++ b/tutorials/serena-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Serena Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Serena running as an MCP server so your agent can use semantic code tools immediately.
 
 ## Learning Goals
@@ -47,3 +50,592 @@ uvx --from git+https://github.com/oraios/serena serena start-mcp-server --help
 You now have Serena launched and connected as an MCP server.
 
 Next: [Chapter 2: Semantic Toolkit and Agent Loop](02-semantic-toolkit-and-agent-loop.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- tutorial slug: **serena-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Serena Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Serena Repository](https://github.com/oraios/serena)
+- [Serena Documentation](https://oraios.github.io/serena/)
+- [Quick Start and MCP startup](https://github.com/oraios/serena/blob/main/README.md#quick-start)
+- [Connecting clients](https://oraios.github.io/serena/02-usage/030_clients.html)
+- [Project workflow](https://oraios.github.io/serena/02-usage/040_workflow.html)
+- [Configuration](https://oraios.github.io/serena/02-usage/050_configuration.html)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Goose Tutorial](../goose-tutorial/)
+- [Crush Tutorial](../crush-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `serena`, `https`, `github` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `oraios`, `start`, `server` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `serena`.
+2. **Input normalization**: shape incoming data so `https` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `github`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Serena Repository](https://github.com/oraios/serena)
+  Why it matters: authoritative reference on `Serena Repository` (github.com).
+- [Serena Documentation](https://oraios.github.io/serena/)
+  Why it matters: authoritative reference on `Serena Documentation` (oraios.github.io).
+- [Quick Start and MCP startup](https://github.com/oraios/serena/blob/main/README.md#quick-start)
+  Why it matters: authoritative reference on `Quick Start and MCP startup` (github.com).
+- [Connecting clients](https://oraios.github.io/serena/02-usage/030_clients.html)
+  Why it matters: authoritative reference on `Connecting clients` (oraios.github.io).
+- [Project workflow](https://oraios.github.io/serena/02-usage/040_workflow.html)
+  Why it matters: authoritative reference on `Project workflow` (oraios.github.io).
+- [Configuration](https://oraios.github.io/serena/02-usage/050_configuration.html)
+  Why it matters: authoritative reference on `Configuration` (oraios.github.io).
+
+Suggested trace strategy:
+- search upstream code for `serena` and `https` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Semantic Toolkit and Agent Loop](02-semantic-toolkit-and-agent-loop.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/serena-tutorial/02-semantic-toolkit-and-agent-loop.md b/tutorials/serena-tutorial/02-semantic-toolkit-and-agent-loop.md
index e5a17da4..094f350f 100644
--- a/tutorials/serena-tutorial/02-semantic-toolkit-and-agent-loop.md
+++ b/tutorials/serena-tutorial/02-semantic-toolkit-and-agent-loop.md
@@ -7,6 +7,9 @@ parent: Serena Tutorial
 
 # Chapter 2: Semantic Toolkit and Agent Loop
 
+Welcome to **Chapter 2: Semantic Toolkit and Agent Loop**. In this part of **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains why Serena materially changes coding-agent behavior in large repositories.
 
 ## Learning Goals
@@ -44,3 +47,589 @@ These tools reduce brute-force full-file scanning and improve edit precision.
 You now understand Serena's core leverage: semantic precision instead of file-wide approximation.
 
 Next: [Chapter 3: MCP Client Integrations](03-mcp-client-integrations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- tutorial slug: **serena-tutorial**
+- chapter focus: **Chapter 2: Semantic Toolkit and Agent Loop**
+- system context: **Serena Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Semantic Toolkit and Agent Loop`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Serena Repository](https://github.com/oraios/serena)
+- [Serena Documentation](https://oraios.github.io/serena/)
+- [Quick Start and MCP startup](https://github.com/oraios/serena/blob/main/README.md#quick-start)
+- [Connecting clients](https://oraios.github.io/serena/02-usage/030_clients.html)
+- [Project workflow](https://oraios.github.io/serena/02-usage/040_workflow.html)
+- [Configuration](https://oraios.github.io/serena/02-usage/050_configuration.html)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Goose Tutorial](../goose-tutorial/)
+- [Crush Tutorial](../crush-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Semantic Toolkit and Agent Loop`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Semantic Toolkit and Agent Loop
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Semantic Toolkit and Agent Loop` as an operating subsystem inside **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Semantic Toolkit and Agent Loop` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Serena Repository](https://github.com/oraios/serena)
+  Why it matters: authoritative reference on `Serena Repository` (github.com).
+- [Serena Documentation](https://oraios.github.io/serena/)
+  Why it matters: authoritative reference on `Serena Documentation` (oraios.github.io).
+- [Quick Start and MCP startup](https://github.com/oraios/serena/blob/main/README.md#quick-start)
+  Why it matters: authoritative reference on `Quick Start and MCP startup` (github.com).
+- [Connecting clients](https://oraios.github.io/serena/02-usage/030_clients.html)
+  Why it matters: authoritative reference on `Connecting clients` (oraios.github.io).
+- [Project workflow](https://oraios.github.io/serena/02-usage/040_workflow.html)
+  Why it matters: authoritative reference on `Project workflow` (oraios.github.io).
+- [Configuration](https://oraios.github.io/serena/02-usage/050_configuration.html)
+  Why it matters: authoritative reference on `Configuration` (oraios.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: MCP Client Integrations](03-mcp-client-integrations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/serena-tutorial/03-mcp-client-integrations.md b/tutorials/serena-tutorial/03-mcp-client-integrations.md
index 6c978eac..28c36e64 100644
--- a/tutorials/serena-tutorial/03-mcp-client-integrations.md
+++ b/tutorials/serena-tutorial/03-mcp-client-integrations.md
@@ -7,6 +7,9 @@ parent: Serena Tutorial
 
 # Chapter 3: MCP Client Integrations
 
+Welcome to **Chapter 3: MCP Client Integrations**. In this part of **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter shows how Serena is deployed as a shared capability layer across different agent surfaces.
 
 ## Learning Goals
@@ -44,3 +47,589 @@ Serena documentation and README list integrations with:
 You now know how Serena fits across multiple agent clients without locking into a single UI.
 
 Next: [Chapter 4: Language Backends and Analysis Strategy](04-language-backends-and-analysis-strategy.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- tutorial slug: **serena-tutorial**
+- chapter focus: **Chapter 3: MCP Client Integrations**
+- system context: **Serena Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: MCP Client Integrations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Serena Repository](https://github.com/oraios/serena)
+- [Serena Documentation](https://oraios.github.io/serena/)
+- [Quick Start and MCP startup](https://github.com/oraios/serena/blob/main/README.md#quick-start)
+- [Connecting clients](https://oraios.github.io/serena/02-usage/030_clients.html)
+- [Project workflow](https://oraios.github.io/serena/02-usage/040_workflow.html)
+- [Configuration](https://oraios.github.io/serena/02-usage/050_configuration.html)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Goose Tutorial](../goose-tutorial/)
+- [Crush Tutorial](../crush-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: MCP Client Integrations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: MCP Client Integrations
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: MCP Client Integrations` as an operating subsystem inside **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: MCP Client Integrations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Serena Repository](https://github.com/oraios/serena)
+  Why it matters: authoritative reference on `Serena Repository` (github.com).
+- [Serena Documentation](https://oraios.github.io/serena/)
+  Why it matters: authoritative reference on `Serena Documentation` (oraios.github.io).
+- [Quick Start and MCP startup](https://github.com/oraios/serena/blob/main/README.md#quick-start)
+  Why it matters: authoritative reference on `Quick Start and MCP startup` (github.com).
+- [Connecting clients](https://oraios.github.io/serena/02-usage/030_clients.html)
+  Why it matters: authoritative reference on `Connecting clients` (oraios.github.io).
+- [Project workflow](https://oraios.github.io/serena/02-usage/040_workflow.html)
+  Why it matters: authoritative reference on `Project workflow` (oraios.github.io).
+- [Configuration](https://oraios.github.io/serena/02-usage/050_configuration.html)
+  Why it matters: authoritative reference on `Configuration` (oraios.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Semantic Toolkit and Agent Loop](02-semantic-toolkit-and-agent-loop.md)
+- [Next Chapter: Chapter 4: Language Backends and Analysis Strategy](04-language-backends-and-analysis-strategy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/serena-tutorial/04-language-backends-and-analysis-strategy.md b/tutorials/serena-tutorial/04-language-backends-and-analysis-strategy.md
index ffb78ba0..f17a1de6 100644
--- a/tutorials/serena-tutorial/04-language-backends-and-analysis-strategy.md
+++ b/tutorials/serena-tutorial/04-language-backends-and-analysis-strategy.md
@@ -7,6 +7,9 @@ parent: Serena Tutorial
 
 # Chapter 4: Language Backends and Analysis Strategy
 
+Welcome to **Chapter 4: Language Backends and Analysis Strategy**. In this part of **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers the backend choices that determine semantic quality and operational complexity.
 
 ## Learning Goals
@@ -41,3 +44,601 @@ Serena reports support for 30+ languages through its LSP abstraction.
 You now can select analysis backend strategy based on workflow, language set, and team environment.
 
 Next: [Chapter 5: Project Workflow and Context Practices](05-project-workflow-and-context-practices.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- tutorial slug: **serena-tutorial**
+- chapter focus: **Chapter 4: Language Backends and Analysis Strategy**
+- system context: **Serena Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Language Backends and Analysis Strategy`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Serena Repository](https://github.com/oraios/serena)
+- [Serena Documentation](https://oraios.github.io/serena/)
+- [Quick Start and MCP startup](https://github.com/oraios/serena/blob/main/README.md#quick-start)
+- [Connecting clients](https://oraios.github.io/serena/02-usage/030_clients.html)
+- [Project workflow](https://oraios.github.io/serena/02-usage/040_workflow.html)
+- [Configuration](https://oraios.github.io/serena/02-usage/050_configuration.html)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Goose Tutorial](../goose-tutorial/)
+- [Crush Tutorial](../crush-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Language Backends and Analysis Strategy`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Language Backends and Analysis Strategy
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Language Backends and Analysis Strategy` as an operating subsystem inside **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Language Backends and Analysis Strategy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Serena Repository](https://github.com/oraios/serena)
+  Why it matters: authoritative reference on `Serena Repository` (github.com).
+- [Serena Documentation](https://oraios.github.io/serena/)
+  Why it matters: authoritative reference on `Serena Documentation` (oraios.github.io).
+- [Quick Start and MCP startup](https://github.com/oraios/serena/blob/main/README.md#quick-start)
+  Why it matters: authoritative reference on `Quick Start and MCP startup` (github.com).
+- [Connecting clients](https://oraios.github.io/serena/02-usage/030_clients.html)
+  Why it matters: authoritative reference on `Connecting clients` (oraios.github.io).
+- [Project workflow](https://oraios.github.io/serena/02-usage/040_workflow.html)
+  Why it matters: authoritative reference on `Project workflow` (oraios.github.io).
+- [Configuration](https://oraios.github.io/serena/02-usage/050_configuration.html)
+  Why it matters: authoritative reference on `Configuration` (oraios.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: MCP Client Integrations](03-mcp-client-integrations.md)
+- [Next Chapter: Chapter 5: Project Workflow and Context Practices](05-project-workflow-and-context-practices.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/serena-tutorial/05-project-workflow-and-context-practices.md b/tutorials/serena-tutorial/05-project-workflow-and-context-practices.md
index 0073439d..d1c8093d 100644
--- a/tutorials/serena-tutorial/05-project-workflow-and-context-practices.md
+++ b/tutorials/serena-tutorial/05-project-workflow-and-context-practices.md
@@ -7,6 +7,9 @@ parent: Serena Tutorial
 
 # Chapter 5: Project Workflow and Context Practices
 
+Welcome to **Chapter 5: Project Workflow and Context Practices**. In this part of **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on day-to-day operating habits that maximize Serena's value.
 
 ## Learning Goals
@@ -41,3 +44,601 @@ Serena is especially effective when:
 You now have practical workflow patterns for getting consistent value from Serena.
 
 Next: [Chapter 6: Configuration and Operational Controls](06-configuration-and-operational-controls.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- tutorial slug: **serena-tutorial**
+- chapter focus: **Chapter 5: Project Workflow and Context Practices**
+- system context: **Serena Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Project Workflow and Context Practices`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Serena Repository](https://github.com/oraios/serena)
+- [Serena Documentation](https://oraios.github.io/serena/)
+- [Quick Start and MCP startup](https://github.com/oraios/serena/blob/main/README.md#quick-start)
+- [Connecting clients](https://oraios.github.io/serena/02-usage/030_clients.html)
+- [Project workflow](https://oraios.github.io/serena/02-usage/040_workflow.html)
+- [Configuration](https://oraios.github.io/serena/02-usage/050_configuration.html)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Goose Tutorial](../goose-tutorial/)
+- [Crush Tutorial](../crush-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Project Workflow and Context Practices`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Project Workflow and Context Practices
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Project Workflow and Context Practices` as an operating subsystem inside **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Project Workflow and Context Practices` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Serena Repository](https://github.com/oraios/serena)
+  Why it matters: authoritative reference on `Serena Repository` (github.com).
+- [Serena Documentation](https://oraios.github.io/serena/)
+  Why it matters: authoritative reference on `Serena Documentation` (oraios.github.io).
+- [Quick Start and MCP startup](https://github.com/oraios/serena/blob/main/README.md#quick-start)
+  Why it matters: authoritative reference on `Quick Start and MCP startup` (github.com).
+- [Connecting clients](https://oraios.github.io/serena/02-usage/030_clients.html)
+  Why it matters: authoritative reference on `Connecting clients` (oraios.github.io).
+- [Project workflow](https://oraios.github.io/serena/02-usage/040_workflow.html)
+  Why it matters: authoritative reference on `Project workflow` (oraios.github.io).
+- [Configuration](https://oraios.github.io/serena/02-usage/050_configuration.html)
+  Why it matters: authoritative reference on `Configuration` (oraios.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Language Backends and Analysis Strategy](04-language-backends-and-analysis-strategy.md)
+- [Next Chapter: Chapter 6: Configuration and Operational Controls](06-configuration-and-operational-controls.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/serena-tutorial/06-configuration-and-operational-controls.md b/tutorials/serena-tutorial/06-configuration-and-operational-controls.md
index 384650cc..c4768509 100644
--- a/tutorials/serena-tutorial/06-configuration-and-operational-controls.md
+++ b/tutorials/serena-tutorial/06-configuration-and-operational-controls.md
@@ -7,6 +7,9 @@ parent: Serena Tutorial
 
 # Chapter 6: Configuration and Operational Controls
 
+Welcome to **Chapter 6: Configuration and Operational Controls**. In this part of **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers configuration strategy for reliability, reproducibility, and team-scale use.
 
 ## Learning Goals
@@ -41,3 +44,601 @@ This chapter covers configuration strategy for reliability, reproducibility, and
 You now have a configuration governance baseline for Serena deployments.
 
 Next: [Chapter 7: Extending Serena and Custom Agent Integration](07-extending-serena-and-custom-agent-integration.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- tutorial slug: **serena-tutorial**
+- chapter focus: **Chapter 6: Configuration and Operational Controls**
+- system context: **Serena Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Configuration and Operational Controls`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Serena Repository](https://github.com/oraios/serena)
+- [Serena Documentation](https://oraios.github.io/serena/)
+- [Quick Start and MCP startup](https://github.com/oraios/serena/blob/main/README.md#quick-start)
+- [Connecting clients](https://oraios.github.io/serena/02-usage/030_clients.html)
+- [Project workflow](https://oraios.github.io/serena/02-usage/040_workflow.html)
+- [Configuration](https://oraios.github.io/serena/02-usage/050_configuration.html)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Goose Tutorial](../goose-tutorial/)
+- [Crush Tutorial](../crush-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Configuration and Operational Controls`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Configuration and Operational Controls
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Configuration and Operational Controls` as an operating subsystem inside **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Configuration and Operational Controls` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Serena Repository](https://github.com/oraios/serena)
+  Why it matters: authoritative reference on `Serena Repository` (github.com).
+- [Serena Documentation](https://oraios.github.io/serena/)
+  Why it matters: authoritative reference on `Serena Documentation` (oraios.github.io).
+- [Quick Start and MCP startup](https://github.com/oraios/serena/blob/main/README.md#quick-start)
+  Why it matters: authoritative reference on `Quick Start and MCP startup` (github.com).
+- [Connecting clients](https://oraios.github.io/serena/02-usage/030_clients.html)
+  Why it matters: authoritative reference on `Connecting clients` (oraios.github.io).
+- [Project workflow](https://oraios.github.io/serena/02-usage/040_workflow.html)
+  Why it matters: authoritative reference on `Project workflow` (oraios.github.io).
+- [Configuration](https://oraios.github.io/serena/02-usage/050_configuration.html)
+  Why it matters: authoritative reference on `Configuration` (oraios.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Project Workflow and Context Practices](05-project-workflow-and-context-practices.md)
+- [Next Chapter: Chapter 7: Extending Serena and Custom Agent Integration](07-extending-serena-and-custom-agent-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/serena-tutorial/07-extending-serena-and-custom-agent-integration.md b/tutorials/serena-tutorial/07-extending-serena-and-custom-agent-integration.md
index ea638707..e978053c 100644
--- a/tutorials/serena-tutorial/07-extending-serena-and-custom-agent-integration.md
+++ b/tutorials/serena-tutorial/07-extending-serena-and-custom-agent-integration.md
@@ -7,6 +7,9 @@ parent: Serena Tutorial
 
 # Chapter 7: Extending Serena and Custom Agent Integration
 
+Welcome to **Chapter 7: Extending Serena and Custom Agent Integration**. In this part of **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter targets advanced users integrating Serena into custom frameworks or extending tool capabilities.
 
 ## Learning Goals
@@ -39,3 +42,601 @@ Serena documents tool extension via subclassing and implementing tool behavior m
 You now know how to plug Serena into bespoke agent systems and extend it safely.
 
 Next: [Chapter 8: Production Operations and Governance](08-production-operations-and-governance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- tutorial slug: **serena-tutorial**
+- chapter focus: **Chapter 7: Extending Serena and Custom Agent Integration**
+- system context: **Serena Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Extending Serena and Custom Agent Integration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Serena Repository](https://github.com/oraios/serena)
+- [Serena Documentation](https://oraios.github.io/serena/)
+- [Quick Start and MCP startup](https://github.com/oraios/serena/blob/main/README.md#quick-start)
+- [Connecting clients](https://oraios.github.io/serena/02-usage/030_clients.html)
+- [Project workflow](https://oraios.github.io/serena/02-usage/040_workflow.html)
+- [Configuration](https://oraios.github.io/serena/02-usage/050_configuration.html)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Goose Tutorial](../goose-tutorial/)
+- [Crush Tutorial](../crush-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Extending Serena and Custom Agent Integration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Extending Serena and Custom Agent Integration
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Extending Serena and Custom Agent Integration` as an operating subsystem inside **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Extending Serena and Custom Agent Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Serena Repository](https://github.com/oraios/serena)
+  Why it matters: authoritative reference on `Serena Repository` (github.com).
+- [Serena Documentation](https://oraios.github.io/serena/)
+  Why it matters: authoritative reference on `Serena Documentation` (oraios.github.io).
+- [Quick Start and MCP startup](https://github.com/oraios/serena/blob/main/README.md#quick-start)
+  Why it matters: authoritative reference on `Quick Start and MCP startup` (github.com).
+- [Connecting clients](https://oraios.github.io/serena/02-usage/030_clients.html)
+  Why it matters: authoritative reference on `Connecting clients` (oraios.github.io).
+- [Project workflow](https://oraios.github.io/serena/02-usage/040_workflow.html)
+  Why it matters: authoritative reference on `Project workflow` (oraios.github.io).
+- [Configuration](https://oraios.github.io/serena/02-usage/050_configuration.html)
+  Why it matters: authoritative reference on `Configuration` (oraios.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Configuration and Operational Controls](06-configuration-and-operational-controls.md)
+- [Next Chapter: Chapter 8: Production Operations and Governance](08-production-operations-and-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/serena-tutorial/08-production-operations-and-governance.md b/tutorials/serena-tutorial/08-production-operations-and-governance.md
index 27f8b8b2..6a4647d9 100644
--- a/tutorials/serena-tutorial/08-production-operations-and-governance.md
+++ b/tutorials/serena-tutorial/08-production-operations-and-governance.md
@@ -7,6 +7,9 @@ parent: Serena Tutorial
 
 # Chapter 8: Production Operations and Governance
 
+Welcome to **Chapter 8: Production Operations and Governance**. In this part of **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter provides a practical rollout model for Serena in high-stakes engineering environments.
 
 ## Learning Goals
@@ -43,3 +46,600 @@ This chapter provides a practical rollout model for Serena in high-stakes engine
 You now have a complete operational model for deploying Serena as a production-grade capability layer.
 
 Continue with the [Onlook Tutorial](../onlook-tutorial/) for visual-first coding workflows.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- tutorial slug: **serena-tutorial**
+- chapter focus: **Chapter 8: Production Operations and Governance**
+- system context: **Serena Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Operations and Governance`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Serena Repository](https://github.com/oraios/serena)
+- [Serena Documentation](https://oraios.github.io/serena/)
+- [Quick Start and MCP startup](https://github.com/oraios/serena/blob/main/README.md#quick-start)
+- [Connecting clients](https://oraios.github.io/serena/02-usage/030_clients.html)
+- [Project workflow](https://oraios.github.io/serena/02-usage/040_workflow.html)
+- [Configuration](https://oraios.github.io/serena/02-usage/050_configuration.html)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Goose Tutorial](../goose-tutorial/)
+- [Crush Tutorial](../crush-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Operations and Governance`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Operations and Governance` as an operating subsystem inside **Serena Tutorial: Semantic Code Retrieval Toolkit for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Operations and Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Serena Repository](https://github.com/oraios/serena)
+  Why it matters: authoritative reference on `Serena Repository` (github.com).
+- [Serena Documentation](https://oraios.github.io/serena/)
+  Why it matters: authoritative reference on `Serena Documentation` (oraios.github.io).
+- [Quick Start and MCP startup](https://github.com/oraios/serena/blob/main/README.md#quick-start)
+  Why it matters: authoritative reference on `Quick Start and MCP startup` (github.com).
+- [Connecting clients](https://oraios.github.io/serena/02-usage/030_clients.html)
+  Why it matters: authoritative reference on `Connecting clients` (oraios.github.io).
+- [Project workflow](https://oraios.github.io/serena/02-usage/040_workflow.html)
+  Why it matters: authoritative reference on `Project workflow` (oraios.github.io).
+- [Configuration](https://oraios.github.io/serena/02-usage/050_configuration.html)
+  Why it matters: authoritative reference on `Configuration` (oraios.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Extending Serena and Custom Agent Integration](07-extending-serena-and-custom-agent-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/shotgun-tutorial/01-getting-started.md b/tutorials/shotgun-tutorial/01-getting-started.md
index 3e6cf7d7..fa8c71bc 100644
--- a/tutorials/shotgun-tutorial/01-getting-started.md
+++ b/tutorials/shotgun-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Shotgun Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Shotgun Tutorial: Spec-Driven Development for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Shotgun running in a repository so you can generate your first spec-driven workflow.
 
 ## Quick Start
@@ -44,3 +47,601 @@ Research how authentication currently works and propose a staged implementation
 You now have Shotgun running with a first research and planning loop.
 
 Next: [Chapter 2: Router Architecture and Agent Lifecycle](02-router-architecture-and-agent-lifecycle.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- tutorial slug: **shotgun-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Shotgun Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Shotgun Repository](https://github.com/shotgun-sh/shotgun)
+- [Shotgun CLI Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CLI.md)
+- [Context7 Integration Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/context7-mcp-integration.md)
+- [Ollama/Local Models Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/ollama-local-models.md)
+- [CI/CD Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CI_CD.md)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [HumanLayer Tutorial](../humanlayer-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `shotgun`, `latest`, `Research` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Shotgun Tutorial: Spec-Driven Development for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `authentication`, `currently`, `works` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `shotgun`.
+2. **Input normalization**: shape incoming data so `latest` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Research`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Shotgun Repository](https://github.com/shotgun-sh/shotgun)
+  Why it matters: authoritative reference on `Shotgun Repository` (github.com).
+- [Shotgun CLI Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CLI.md)
+  Why it matters: authoritative reference on `Shotgun CLI Docs` (github.com).
+- [Context7 Integration Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/context7-mcp-integration.md)
+  Why it matters: authoritative reference on `Context7 Integration Architecture` (github.com).
+- [Ollama/Local Models Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/ollama-local-models.md)
+  Why it matters: authoritative reference on `Ollama/Local Models Architecture` (github.com).
+- [CI/CD Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CI_CD.md)
+  Why it matters: authoritative reference on `CI/CD Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `shotgun` and `latest` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Router Architecture and Agent Lifecycle](02-router-architecture-and-agent-lifecycle.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/shotgun-tutorial/02-router-architecture-and-agent-lifecycle.md b/tutorials/shotgun-tutorial/02-router-architecture-and-agent-lifecycle.md
index 3501b26c..9964d490 100644
--- a/tutorials/shotgun-tutorial/02-router-architecture-and-agent-lifecycle.md
+++ b/tutorials/shotgun-tutorial/02-router-architecture-and-agent-lifecycle.md
@@ -7,6 +7,9 @@ parent: Shotgun Tutorial
 
 # Chapter 2: Router Architecture and Agent Lifecycle
 
+Welcome to **Chapter 2: Router Architecture and Agent Lifecycle**. In this part of **Shotgun Tutorial: Spec-Driven Development for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Shotgun routes requests through specialized agents instead of using one generic prompt loop.
 
 ## Lifecycle Model
@@ -39,3 +42,598 @@ Shotgun documentation describes a router that orchestrates these phases internal
 You now understand how Shotgun sequences specialized agents across the delivery lifecycle.
 
 Next: [Chapter 3: Planning vs Drafting Execution Modes](03-planning-vs-drafting-execution-modes.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- tutorial slug: **shotgun-tutorial**
+- chapter focus: **Chapter 2: Router Architecture and Agent Lifecycle**
+- system context: **Shotgun Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Router Architecture and Agent Lifecycle`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Shotgun Repository](https://github.com/shotgun-sh/shotgun)
+- [Shotgun CLI Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CLI.md)
+- [Context7 Integration Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/context7-mcp-integration.md)
+- [Ollama/Local Models Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/ollama-local-models.md)
+- [CI/CD Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CI_CD.md)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [HumanLayer Tutorial](../humanlayer-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Router Architecture and Agent Lifecycle`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Router Architecture and Agent Lifecycle
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Router Architecture and Agent Lifecycle` as an operating subsystem inside **Shotgun Tutorial: Spec-Driven Development for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Router Architecture and Agent Lifecycle` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Shotgun Repository](https://github.com/shotgun-sh/shotgun)
+  Why it matters: authoritative reference on `Shotgun Repository` (github.com).
+- [Shotgun CLI Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CLI.md)
+  Why it matters: authoritative reference on `Shotgun CLI Docs` (github.com).
+- [Context7 Integration Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/context7-mcp-integration.md)
+  Why it matters: authoritative reference on `Context7 Integration Architecture` (github.com).
+- [Ollama/Local Models Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/ollama-local-models.md)
+  Why it matters: authoritative reference on `Ollama/Local Models Architecture` (github.com).
+- [CI/CD Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CI_CD.md)
+  Why it matters: authoritative reference on `CI/CD Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Planning vs Drafting Execution Modes](03-planning-vs-drafting-execution-modes.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/shotgun-tutorial/03-planning-vs-drafting-execution-modes.md b/tutorials/shotgun-tutorial/03-planning-vs-drafting-execution-modes.md
index 41b817a0..414a9d12 100644
--- a/tutorials/shotgun-tutorial/03-planning-vs-drafting-execution-modes.md
+++ b/tutorials/shotgun-tutorial/03-planning-vs-drafting-execution-modes.md
@@ -7,6 +7,9 @@ parent: Shotgun Tutorial
 
 # Chapter 3: Planning vs Drafting Execution Modes
 
+Welcome to **Chapter 3: Planning vs Drafting Execution Modes**. In this part of **Shotgun Tutorial: Spec-Driven Development for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Shotgun exposes two user-facing execution modes with different tradeoffs.
 
 ## Mode Comparison
@@ -45,3 +48,586 @@ Use Drafting when:
 You can now choose execution mode based on risk, ambiguity, and throughput needs.
 
 Next: [Chapter 4: Codebase Indexing and Context Retrieval](04-codebase-indexing-and-context-retrieval.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- tutorial slug: **shotgun-tutorial**
+- chapter focus: **Chapter 3: Planning vs Drafting Execution Modes**
+- system context: **Shotgun Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Planning vs Drafting Execution Modes`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Shotgun Repository](https://github.com/shotgun-sh/shotgun)
+- [Shotgun CLI Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CLI.md)
+- [Context7 Integration Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/context7-mcp-integration.md)
+- [Ollama/Local Models Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/ollama-local-models.md)
+- [CI/CD Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CI_CD.md)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [HumanLayer Tutorial](../humanlayer-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Planning vs Drafting Execution Modes`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Planning vs Drafting Execution Modes
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Planning vs Drafting Execution Modes` as an operating subsystem inside **Shotgun Tutorial: Spec-Driven Development for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Planning vs Drafting Execution Modes` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Shotgun Repository](https://github.com/shotgun-sh/shotgun)
+  Why it matters: authoritative reference on `Shotgun Repository` (github.com).
+- [Shotgun CLI Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CLI.md)
+  Why it matters: authoritative reference on `Shotgun CLI Docs` (github.com).
+- [Context7 Integration Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/context7-mcp-integration.md)
+  Why it matters: authoritative reference on `Context7 Integration Architecture` (github.com).
+- [Ollama/Local Models Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/ollama-local-models.md)
+  Why it matters: authoritative reference on `Ollama/Local Models Architecture` (github.com).
+- [CI/CD Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CI_CD.md)
+  Why it matters: authoritative reference on `CI/CD Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Router Architecture and Agent Lifecycle](02-router-architecture-and-agent-lifecycle.md)
+- [Next Chapter: Chapter 4: Codebase Indexing and Context Retrieval](04-codebase-indexing-and-context-retrieval.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/shotgun-tutorial/04-codebase-indexing-and-context-retrieval.md b/tutorials/shotgun-tutorial/04-codebase-indexing-and-context-retrieval.md
index e56e4210..4eaed502 100644
--- a/tutorials/shotgun-tutorial/04-codebase-indexing-and-context-retrieval.md
+++ b/tutorials/shotgun-tutorial/04-codebase-indexing-and-context-retrieval.md
@@ -7,6 +7,9 @@ parent: Shotgun Tutorial
 
 # Chapter 4: Codebase Indexing and Context Retrieval
 
+Welcome to **Chapter 4: Codebase Indexing and Context Retrieval**. In this part of **Shotgun Tutorial: Spec-Driven Development for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Shotgun builds a local code graph so agent outputs are grounded in actual repository structure.
 
 ## Indexing Workflow
@@ -35,3 +38,598 @@ Shotgun docs emphasize local indexing storage and no code upload during indexing
 You now understand how codebase indexing improves planning and reduces execution drift.
 
 Next: [Chapter 5: CLI Automation and Scripting](05-cli-automation-and-scripting.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- tutorial slug: **shotgun-tutorial**
+- chapter focus: **Chapter 4: Codebase Indexing and Context Retrieval**
+- system context: **Shotgun Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Codebase Indexing and Context Retrieval`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Shotgun Repository](https://github.com/shotgun-sh/shotgun)
+- [Shotgun CLI Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CLI.md)
+- [Context7 Integration Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/context7-mcp-integration.md)
+- [Ollama/Local Models Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/ollama-local-models.md)
+- [CI/CD Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CI_CD.md)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [HumanLayer Tutorial](../humanlayer-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Codebase Indexing and Context Retrieval`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Codebase Indexing and Context Retrieval
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Codebase Indexing and Context Retrieval` as an operating subsystem inside **Shotgun Tutorial: Spec-Driven Development for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Codebase Indexing and Context Retrieval` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Shotgun Repository](https://github.com/shotgun-sh/shotgun)
+  Why it matters: authoritative reference on `Shotgun Repository` (github.com).
+- [Shotgun CLI Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CLI.md)
+  Why it matters: authoritative reference on `Shotgun CLI Docs` (github.com).
+- [Context7 Integration Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/context7-mcp-integration.md)
+  Why it matters: authoritative reference on `Context7 Integration Architecture` (github.com).
+- [Ollama/Local Models Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/ollama-local-models.md)
+  Why it matters: authoritative reference on `Ollama/Local Models Architecture` (github.com).
+- [CI/CD Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CI_CD.md)
+  Why it matters: authoritative reference on `CI/CD Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Planning vs Drafting Execution Modes](03-planning-vs-drafting-execution-modes.md)
+- [Next Chapter: Chapter 5: CLI Automation and Scripting](05-cli-automation-and-scripting.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/shotgun-tutorial/05-cli-automation-and-scripting.md b/tutorials/shotgun-tutorial/05-cli-automation-and-scripting.md
index 227b31c0..d724febe 100644
--- a/tutorials/shotgun-tutorial/05-cli-automation-and-scripting.md
+++ b/tutorials/shotgun-tutorial/05-cli-automation-and-scripting.md
@@ -7,6 +7,9 @@ parent: Shotgun Tutorial
 
 # Chapter 5: CLI Automation and Scripting
 
+Welcome to **Chapter 5: CLI Automation and Scripting**. In this part of **Shotgun Tutorial: Spec-Driven Development for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Shotgun includes CLI commands for non-interactive and automation-friendly usage.
 
 ## Key Commands
@@ -36,3 +39,602 @@ Use `shotgun run -n` in controlled environments where deterministic prompt templ
 You can now run Shotgun workflows both interactively and in scripted pipelines.
 
 Next: [Chapter 6: Context7 MCP and Local Models](06-context7-mcp-and-local-models.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- tutorial slug: **shotgun-tutorial**
+- chapter focus: **Chapter 5: CLI Automation and Scripting**
+- system context: **Shotgun Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: CLI Automation and Scripting`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Shotgun Repository](https://github.com/shotgun-sh/shotgun)
+- [Shotgun CLI Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CLI.md)
+- [Context7 Integration Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/context7-mcp-integration.md)
+- [Ollama/Local Models Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/ollama-local-models.md)
+- [CI/CD Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CI_CD.md)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [HumanLayer Tutorial](../humanlayer-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: CLI Automation and Scripting`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: CLI Automation and Scripting
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `shotgun`, `plan`, `Research` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: CLI Automation and Scripting` as an operating subsystem inside **Shotgun Tutorial: Spec-Driven Development for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `auth`, `architecture`, `produce` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: CLI Automation and Scripting` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `shotgun`.
+2. **Input normalization**: shape incoming data so `plan` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Research`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Shotgun Repository](https://github.com/shotgun-sh/shotgun)
+  Why it matters: authoritative reference on `Shotgun Repository` (github.com).
+- [Shotgun CLI Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CLI.md)
+  Why it matters: authoritative reference on `Shotgun CLI Docs` (github.com).
+- [Context7 Integration Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/context7-mcp-integration.md)
+  Why it matters: authoritative reference on `Context7 Integration Architecture` (github.com).
+- [Ollama/Local Models Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/ollama-local-models.md)
+  Why it matters: authoritative reference on `Ollama/Local Models Architecture` (github.com).
+- [CI/CD Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CI_CD.md)
+  Why it matters: authoritative reference on `CI/CD Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `shotgun` and `plan` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Codebase Indexing and Context Retrieval](04-codebase-indexing-and-context-retrieval.md)
+- [Next Chapter: Chapter 6: Context7 MCP and Local Models](06-context7-mcp-and-local-models.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/shotgun-tutorial/06-context7-mcp-and-local-models.md b/tutorials/shotgun-tutorial/06-context7-mcp-and-local-models.md
index 068ce822..10608056 100644
--- a/tutorials/shotgun-tutorial/06-context7-mcp-and-local-models.md
+++ b/tutorials/shotgun-tutorial/06-context7-mcp-and-local-models.md
@@ -7,6 +7,9 @@ parent: Shotgun Tutorial
 
 # Chapter 6: Context7 MCP and Local Models
 
+Welcome to **Chapter 6: Context7 MCP and Local Models**. In this part of **Shotgun Tutorial: Spec-Driven Development for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Shotgun supports live documentation lookup through Context7 MCP and can run local-model workflows through Ollama integration.
 
 ## Context7 Integration
@@ -33,3 +36,598 @@ Ollama models are exposed via an OpenAI-compatible path with capability detectio
 You now have a model for combining live docs retrieval and local-model execution pathways.
 
 Next: [Chapter 7: Spec Sharing and Collaboration Workflows](07-spec-sharing-and-collaboration-workflows.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- tutorial slug: **shotgun-tutorial**
+- chapter focus: **Chapter 6: Context7 MCP and Local Models**
+- system context: **Shotgun Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Context7 MCP and Local Models`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Shotgun Repository](https://github.com/shotgun-sh/shotgun)
+- [Shotgun CLI Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CLI.md)
+- [Context7 Integration Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/context7-mcp-integration.md)
+- [Ollama/Local Models Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/ollama-local-models.md)
+- [CI/CD Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CI_CD.md)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [HumanLayer Tutorial](../humanlayer-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Context7 MCP and Local Models`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Context7 MCP and Local Models
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Context7 MCP and Local Models` as an operating subsystem inside **Shotgun Tutorial: Spec-Driven Development for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Context7 MCP and Local Models` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Shotgun Repository](https://github.com/shotgun-sh/shotgun)
+  Why it matters: authoritative reference on `Shotgun Repository` (github.com).
+- [Shotgun CLI Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CLI.md)
+  Why it matters: authoritative reference on `Shotgun CLI Docs` (github.com).
+- [Context7 Integration Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/context7-mcp-integration.md)
+  Why it matters: authoritative reference on `Context7 Integration Architecture` (github.com).
+- [Ollama/Local Models Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/ollama-local-models.md)
+  Why it matters: authoritative reference on `Ollama/Local Models Architecture` (github.com).
+- [CI/CD Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CI_CD.md)
+  Why it matters: authoritative reference on `CI/CD Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: CLI Automation and Scripting](05-cli-automation-and-scripting.md)
+- [Next Chapter: Chapter 7: Spec Sharing and Collaboration Workflows](07-spec-sharing-and-collaboration-workflows.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/shotgun-tutorial/07-spec-sharing-and-collaboration-workflows.md b/tutorials/shotgun-tutorial/07-spec-sharing-and-collaboration-workflows.md
index 13757aa3..bedeabd6 100644
--- a/tutorials/shotgun-tutorial/07-spec-sharing-and-collaboration-workflows.md
+++ b/tutorials/shotgun-tutorial/07-spec-sharing-and-collaboration-workflows.md
@@ -7,6 +7,9 @@ parent: Shotgun Tutorial
 
 # Chapter 7: Spec Sharing and Collaboration Workflows
 
+Welcome to **Chapter 7: Spec Sharing and Collaboration Workflows**. In this part of **Shotgun Tutorial: Spec-Driven Development for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Shotgun workflows are designed around reusable, versioned spec artifacts that teams can review and share.
 
 ## Artifact Model
@@ -35,3 +38,598 @@ Shotgun workflows are designed around reusable, versioned spec artifacts that te
 You can now structure multi-person review around stable spec artifacts instead of ad hoc prompts.
 
 Next: [Chapter 8: Production Operations, Observability, and Security](08-production-operations-observability-and-security.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- tutorial slug: **shotgun-tutorial**
+- chapter focus: **Chapter 7: Spec Sharing and Collaboration Workflows**
+- system context: **Shotgun Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Spec Sharing and Collaboration Workflows`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Shotgun Repository](https://github.com/shotgun-sh/shotgun)
+- [Shotgun CLI Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CLI.md)
+- [Context7 Integration Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/context7-mcp-integration.md)
+- [Ollama/Local Models Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/ollama-local-models.md)
+- [CI/CD Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CI_CD.md)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [HumanLayer Tutorial](../humanlayer-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Spec Sharing and Collaboration Workflows`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Spec Sharing and Collaboration Workflows
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Spec Sharing and Collaboration Workflows` as an operating subsystem inside **Shotgun Tutorial: Spec-Driven Development for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Spec Sharing and Collaboration Workflows` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Shotgun Repository](https://github.com/shotgun-sh/shotgun)
+  Why it matters: authoritative reference on `Shotgun Repository` (github.com).
+- [Shotgun CLI Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CLI.md)
+  Why it matters: authoritative reference on `Shotgun CLI Docs` (github.com).
+- [Context7 Integration Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/context7-mcp-integration.md)
+  Why it matters: authoritative reference on `Context7 Integration Architecture` (github.com).
+- [Ollama/Local Models Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/ollama-local-models.md)
+  Why it matters: authoritative reference on `Ollama/Local Models Architecture` (github.com).
+- [CI/CD Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CI_CD.md)
+  Why it matters: authoritative reference on `CI/CD Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Context7 MCP and Local Models](06-context7-mcp-and-local-models.md)
+- [Next Chapter: Chapter 8: Production Operations, Observability, and Security](08-production-operations-observability-and-security.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/shotgun-tutorial/08-production-operations-observability-and-security.md b/tutorials/shotgun-tutorial/08-production-operations-observability-and-security.md
index d1a0c7d2..07adc657 100644
--- a/tutorials/shotgun-tutorial/08-production-operations-observability-and-security.md
+++ b/tutorials/shotgun-tutorial/08-production-operations-observability-and-security.md
@@ -7,6 +7,9 @@ parent: Shotgun Tutorial
 
 # Chapter 8: Production Operations, Observability, and Security
 
+Welcome to **Chapter 8: Production Operations, Observability, and Security**. In this part of **Shotgun Tutorial: Spec-Driven Development for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Production use of Shotgun requires clear controls across CI, runtime telemetry, and deployment boundaries.
 
 ## Production Checklist
@@ -37,3 +40,597 @@ Production use of Shotgun requires clear controls across CI, runtime telemetry,
 ## Summary
 
 You now have an operating baseline for running Shotgun in team and production workflows.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- tutorial slug: **shotgun-tutorial**
+- chapter focus: **Chapter 8: Production Operations, Observability, and Security**
+- system context: **Shotgun Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Operations, Observability, and Security`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Shotgun Repository](https://github.com/shotgun-sh/shotgun)
+- [Shotgun CLI Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CLI.md)
+- [Context7 Integration Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/context7-mcp-integration.md)
+- [Ollama/Local Models Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/ollama-local-models.md)
+- [CI/CD Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CI_CD.md)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [HumanLayer Tutorial](../humanlayer-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Operations, Observability, and Security`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Operations, Observability, and Security
+
+- tutorial context: **Shotgun Tutorial: Spec-Driven Development for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Operations, Observability, and Security` as an operating subsystem inside **Shotgun Tutorial: Spec-Driven Development for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Operations, Observability, and Security` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Shotgun Repository](https://github.com/shotgun-sh/shotgun)
+  Why it matters: authoritative reference on `Shotgun Repository` (github.com).
+- [Shotgun CLI Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CLI.md)
+  Why it matters: authoritative reference on `Shotgun CLI Docs` (github.com).
+- [Context7 Integration Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/context7-mcp-integration.md)
+  Why it matters: authoritative reference on `Context7 Integration Architecture` (github.com).
+- [Ollama/Local Models Architecture](https://github.com/shotgun-sh/shotgun/blob/main/docs/architecture/ollama-local-models.md)
+  Why it matters: authoritative reference on `Ollama/Local Models Architecture` (github.com).
+- [CI/CD Docs](https://github.com/shotgun-sh/shotgun/blob/main/docs/CI_CD.md)
+  Why it matters: authoritative reference on `CI/CD Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Spec Sharing and Collaboration Workflows](07-spec-sharing-and-collaboration-workflows.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/sillytavern-tutorial/01-getting-started.md b/tutorials/sillytavern-tutorial/01-getting-started.md
index 4bf13925..52927935 100644
--- a/tutorials/sillytavern-tutorial/01-getting-started.md
+++ b/tutorials/sillytavern-tutorial/01-getting-started.md
@@ -508,3 +508,52 @@ Ready to create compelling characters? Let's explore [Chapter 2: Character Creat
 5. Customize the interface to your preferences
 
 *What type of character are you most excited to create?* 🎭
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `SillyTavern`, `Chat`, `Character` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with SillyTavern` as an operating subsystem inside **SillyTavern Tutorial: Advanced LLM Frontend for Power Users**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `localhost`, `performance`, `http` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with SillyTavern` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `SillyTavern`.
+2. **Input normalization**: shape incoming data so `Chat` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Character`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/SillyTavern/SillyTavern)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [Extension Directory](https://github.com/SillyTavern/SillyTavern#extensions)
+  Why it matters: authoritative reference on `Extension Directory` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `SillyTavern` and `Chat` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Character Creation](02-character-creation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/sillytavern-tutorial/02-character-creation.md b/tutorials/sillytavern-tutorial/02-character-creation.md
index 5f550613..24040d1c 100644
--- a/tutorials/sillytavern-tutorial/02-character-creation.md
+++ b/tutorials/sillytavern-tutorial/02-character-creation.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Character Creation
 
+Welcome to **Chapter 2: Character Creation**. In this part of **SillyTavern Tutorial: Advanced LLM Frontend for Power Users**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master the art of creating compelling, consistent characters for immersive AI interactions.
 
 ## Overview
@@ -452,3 +455,53 @@ Now that you can create compelling characters, let's explore managing conversati
 **Ready for Chapter 3?** [Chat Management](03-chat-management.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Luna`, `character`, `Crystal` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Character Creation` as an operating subsystem inside **SillyTavern Tutorial: Advanced LLM Frontend for Power Users**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Academy`, `Starweaver`, `magical` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Character Creation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Luna`.
+2. **Input normalization**: shape incoming data so `character` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Crystal`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/SillyTavern/SillyTavern)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [Extension Directory](https://github.com/SillyTavern/SillyTavern#extensions)
+  Why it matters: authoritative reference on `Extension Directory` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Luna` and `character` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with SillyTavern](01-getting-started.md)
+- [Next Chapter: Chapter 3: Chat Management](03-chat-management.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/sillytavern-tutorial/03-chat-management.md b/tutorials/sillytavern-tutorial/03-chat-management.md
index de1980cd..eea2548d 100644
--- a/tutorials/sillytavern-tutorial/03-chat-management.md
+++ b/tutorials/sillytavern-tutorial/03-chat-management.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Chat Management
 
+Welcome to **Chapter 3: Chat Management**. In this part of **SillyTavern Tutorial: Advanced LLM Frontend for Power Users**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master conversation organization, branching, and history management for complex narratives.
 
 ## Overview
@@ -604,3 +607,53 @@ Now that you can manage conversations effectively, let's dive into advanced prom
 **Ready for Chapter 4?** [Prompt Engineering](04-prompt-engineering.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `chat`, `messages`, `message` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Chat Management` as an operating subsystem inside **SillyTavern Tutorial: Advanced LLM Frontend for Power Users**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `chatId`, `role`, `content` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Chat Management` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `chat`.
+2. **Input normalization**: shape incoming data so `messages` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `message`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/SillyTavern/SillyTavern)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [Extension Directory](https://github.com/SillyTavern/SillyTavern#extensions)
+  Why it matters: authoritative reference on `Extension Directory` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `chat` and `messages` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Character Creation](02-character-creation.md)
+- [Next Chapter: Chapter 4: Prompt Engineering](04-prompt-engineering.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/sillytavern-tutorial/04-prompt-engineering.md b/tutorials/sillytavern-tutorial/04-prompt-engineering.md
index ac18f987..b9743978 100644
--- a/tutorials/sillytavern-tutorial/04-prompt-engineering.md
+++ b/tutorials/sillytavern-tutorial/04-prompt-engineering.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Prompt Engineering
 
+Welcome to **Chapter 4: Prompt Engineering**. In this part of **SillyTavern Tutorial: Advanced LLM Frontend for Power Users**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master advanced prompting techniques for optimal AI responses and consistent character behavior.
 
 ## Overview
@@ -492,3 +495,53 @@ Now that you understand prompt engineering, let's explore the extension ecosyste
 **Ready for Chapter 5?** [Extensions Ecosystem](05-extensions-ecosystem.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `messages`, `char`, `prompt` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Prompt Engineering` as an operating subsystem inside **SillyTavern Tutorial: Advanced LLM Frontend for Power Users**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Luna`, `character`, `user` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Prompt Engineering` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `messages`.
+2. **Input normalization**: shape incoming data so `char` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `prompt`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/SillyTavern/SillyTavern)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [Extension Directory](https://github.com/SillyTavern/SillyTavern#extensions)
+  Why it matters: authoritative reference on `Extension Directory` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `messages` and `char` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Chat Management](03-chat-management.md)
+- [Next Chapter: Chapter 5: Extensions Ecosystem](05-extensions-ecosystem.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/sillytavern-tutorial/05-extensions-ecosystem.md b/tutorials/sillytavern-tutorial/05-extensions-ecosystem.md
index 5b7b232e..b2519241 100644
--- a/tutorials/sillytavern-tutorial/05-extensions-ecosystem.md
+++ b/tutorials/sillytavern-tutorial/05-extensions-ecosystem.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Extensions Ecosystem
 
+Welcome to **Chapter 5: Extensions Ecosystem**. In this part of **SillyTavern Tutorial: Advanced LLM Frontend for Power Users**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Discover and utilize SillyTavern's rich extension ecosystem to enhance your experience.
 
 ## Overview
@@ -705,3 +708,53 @@ Now that you understand the extension ecosystem, let's explore setting up multip
 **Ready for Chapter 6?** [Multi-Model Setup](06-multi-model-setup.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `extension`, `color`, `theme` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Extensions Ecosystem` as an operating subsystem inside **SillyTavern Tutorial: Advanced LLM Frontend for Power Users**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `settings`, `manifest`, `character` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Extensions Ecosystem` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `extension`.
+2. **Input normalization**: shape incoming data so `color` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `theme`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/SillyTavern/SillyTavern)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [Extension Directory](https://github.com/SillyTavern/SillyTavern#extensions)
+  Why it matters: authoritative reference on `Extension Directory` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `extension` and `color` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Prompt Engineering](04-prompt-engineering.md)
+- [Next Chapter: Chapter 6: Multi-Model Setup](06-multi-model-setup.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/sillytavern-tutorial/06-multi-model-setup.md b/tutorials/sillytavern-tutorial/06-multi-model-setup.md
index 4649da61..9dff3c40 100644
--- a/tutorials/sillytavern-tutorial/06-multi-model-setup.md
+++ b/tutorials/sillytavern-tutorial/06-multi-model-setup.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Multi-Model Setup
 
+Welcome to **Chapter 6: Multi-Model Setup**. In this part of **SillyTavern Tutorial: Advanced LLM Frontend for Power Users**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Configure and switch between multiple LLM providers for optimal results.
 
 ## Overview
@@ -629,3 +632,53 @@ Now that you can configure multiple models, let's explore advanced power user fe
 **Ready for Chapter 7?** [Advanced Features](07-advanced-features.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `config`, `name`, `provider` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Multi-Model Setup` as an operating subsystem inside **SillyTavern Tutorial: Advanced LLM Frontend for Power Users**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `model`, `parameters`, `temperature` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Multi-Model Setup` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `config`.
+2. **Input normalization**: shape incoming data so `name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `provider`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/SillyTavern/SillyTavern)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [Extension Directory](https://github.com/SillyTavern/SillyTavern#extensions)
+  Why it matters: authoritative reference on `Extension Directory` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `config` and `name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Extensions Ecosystem](05-extensions-ecosystem.md)
+- [Next Chapter: Chapter 7: Advanced Features](07-advanced-features.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/sillytavern-tutorial/07-advanced-features.md b/tutorials/sillytavern-tutorial/07-advanced-features.md
index ce51e544..28214370 100644
--- a/tutorials/sillytavern-tutorial/07-advanced-features.md
+++ b/tutorials/sillytavern-tutorial/07-advanced-features.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Advanced Features
 
+Welcome to **Chapter 7: Advanced Features**. In this part of **SillyTavern Tutorial: Advanced LLM Frontend for Power Users**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master power user features for complex storytelling and advanced AI interactions.
 
 ## Overview
@@ -625,3 +628,53 @@ Ready to create your own extensions? Let's explore custom development in Chapter
 **Ready for Chapter 8?** [Custom Development](08-custom-development.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `name`, `result`, `replace` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Advanced Features` as an operating subsystem inside **SillyTavern Tutorial: Advanced LLM Frontend for Power Users**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `message`, `chat`, `text` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Advanced Features` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `name`.
+2. **Input normalization**: shape incoming data so `result` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `replace`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/SillyTavern/SillyTavern)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [Extension Directory](https://github.com/SillyTavern/SillyTavern#extensions)
+  Why it matters: authoritative reference on `Extension Directory` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `name` and `result` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Multi-Model Setup](06-multi-model-setup.md)
+- [Next Chapter: Chapter 8: Custom Development](08-custom-development.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/sillytavern-tutorial/08-custom-development.md b/tutorials/sillytavern-tutorial/08-custom-development.md
index a9345c18..c5ac1140 100644
--- a/tutorials/sillytavern-tutorial/08-custom-development.md
+++ b/tutorials/sillytavern-tutorial/08-custom-development.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Custom Development
 
+Welcome to **Chapter 8: Custom Development**. In this part of **SillyTavern Tutorial: Advanced LLM Frontend for Power Users**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Learn to create custom extensions, themes, and integrations for SillyTavern.
 
 ## Overview
@@ -855,3 +858,52 @@ Congratulations! 🎉 You've completed the SillyTavern tutorial. You now have th
 ---
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `theme`, `message`, `primary` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Custom Development` as an operating subsystem inside **SillyTavern Tutorial: Advanced LLM Frontend for Power Users**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `settings`, `radius`, `extension` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Custom Development` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `theme`.
+2. **Input normalization**: shape incoming data so `message` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `primary`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/SillyTavern/SillyTavern)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [Extension Directory](https://github.com/SillyTavern/SillyTavern#extensions)
+  Why it matters: authoritative reference on `Extension Directory` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `theme` and `message` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Advanced Features](07-advanced-features.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/siyuan-tutorial/01-getting-started.md b/tutorials/siyuan-tutorial/01-getting-started.md
index 6110c952..3c753ec4 100644
--- a/tutorials/siyuan-tutorial/01-getting-started.md
+++ b/tutorials/siyuan-tutorial/01-getting-started.md
@@ -361,3 +361,48 @@ Now that you understand SiYuan's basics, let's dive deeper into its unique block
 4. Look at the generated database files in your workspace
 
 *What's the most interesting aspect of SiYuan's privacy-first approach?* 🔒
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `siyuan`, `block`, `SiYuan` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with SiYuan` as an operating subsystem inside **SiYuan Tutorial: Privacy-First Knowledge Management**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `content`, `blocks`, `TEXT` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with SiYuan` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `siyuan`.
+2. **Input normalization**: shape incoming data so `block` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `SiYuan`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/siyuan-note/siyuan)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `siyuan` and `block` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Block-Based Architecture](02-block-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/siyuan-tutorial/02-block-architecture.md b/tutorials/siyuan-tutorial/02-block-architecture.md
index f81a3fd9..11b4d7de 100644
--- a/tutorials/siyuan-tutorial/02-block-architecture.md
+++ b/tutorials/siyuan-tutorial/02-block-architecture.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Block-Based Architecture
 
+Welcome to **Chapter 2: Block-Based Architecture**. In this part of **SiYuan Tutorial: Privacy-First Knowledge Management**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 1](01-getting-started.md), we installed SiYuan and explored the basics of creating documents and blocks. Now we'll take a deep dive into SiYuan's most distinctive feature: its block-based architecture. Understanding how blocks work is essential to mastering everything else in SiYuan.
 
 ## Why Blocks Matter
@@ -755,3 +758,49 @@ Now that you understand SiYuan's block architecture, let's explore how all this
 ---
 
 *Built with insights from the [SiYuan](https://github.com/siyuan-note/siyuan) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `block`, `json`, `Block` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Block-Based Architecture` as an operating subsystem inside **SiYuan Tutorial: Privacy-First Knowledge Management**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Node`, `func`, `classDef` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Block-Based Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `block`.
+2. **Input normalization**: shape incoming data so `json` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Block`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/siyuan-note/siyuan)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `block` and `json` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with SiYuan](01-getting-started.md)
+- [Next Chapter: Chapter 3: Data Storage & Persistence](03-data-storage.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/siyuan-tutorial/03-data-storage.md b/tutorials/siyuan-tutorial/03-data-storage.md
index 5cabc294..2beef171 100644
--- a/tutorials/siyuan-tutorial/03-data-storage.md
+++ b/tutorials/siyuan-tutorial/03-data-storage.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Data Storage & Persistence
 
+Welcome to **Chapter 3: Data Storage & Persistence**. In this part of **SiYuan Tutorial: Privacy-First Knowledge Management**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 2](02-block-architecture.md), we explored SiYuan's block-based architecture and how blocks form a tree hierarchy. Now let's look under the hood at how all that data is persisted, organized on disk, and synchronized across devices. SiYuan's storage layer is carefully designed to balance performance, privacy, and portability.
 
 ## Storage Architecture Overview
@@ -1003,3 +1006,49 @@ Now that you understand how SiYuan stores and syncs data, let's explore how to q
 ---
 
 *Built with insights from the [SiYuan](https://github.com/siyuan-note/siyuan) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `TEXT`, `block`, `json` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Data Storage & Persistence` as an operating subsystem inside **SiYuan Tutorial: Privacy-First Knowledge Management**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `blocks`, `path`, `CREATE` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Data Storage & Persistence` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `TEXT`.
+2. **Input normalization**: shape incoming data so `block` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `json`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/siyuan-note/siyuan)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `TEXT` and `block` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Block-Based Architecture](02-block-architecture.md)
+- [Next Chapter: Chapter 4: Query System & Search](04-query-system.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/siyuan-tutorial/04-query-system.md b/tutorials/siyuan-tutorial/04-query-system.md
index d2a09f78..356410ff 100644
--- a/tutorials/siyuan-tutorial/04-query-system.md
+++ b/tutorials/siyuan-tutorial/04-query-system.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Query System & Search
 
+Welcome to **Chapter 4: Query System & Search**. In this part of **SiYuan Tutorial: Privacy-First Knowledge Management**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 3](03-data-storage.md), we explored how SiYuan stores data in SQLite and `.sy` files. Now let's put that database to work. SiYuan's query system is one of its most powerful features -- it lets you treat your knowledge base as a queryable database, not just a collection of notes.
 
 ## Query Architecture
@@ -937,3 +940,49 @@ Now that you can query and search your knowledge base effectively, let's explore
 ---
 
 *Built with insights from the [SiYuan](https://github.com/siyuan-note/siyuan) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `json`, `blocks`, `content` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Query System & Search` as an operating subsystem inside **SiYuan Tutorial: Privacy-First Knowledge Management**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `SELECT`, `query`, `WHERE` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Query System & Search` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `json`.
+2. **Input normalization**: shape incoming data so `blocks` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `content`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/siyuan-note/siyuan)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `json` and `blocks` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Data Storage & Persistence](03-data-storage.md)
+- [Next Chapter: Chapter 5: Plugin Architecture](05-plugin-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/siyuan-tutorial/05-plugin-architecture.md b/tutorials/siyuan-tutorial/05-plugin-architecture.md
index 5b443bb1..110df809 100644
--- a/tutorials/siyuan-tutorial/05-plugin-architecture.md
+++ b/tutorials/siyuan-tutorial/05-plugin-architecture.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Plugin Architecture
 
+Welcome to **Chapter 5: Plugin Architecture**. In this part of **SiYuan Tutorial: Privacy-First Knowledge Management**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 4](04-query-system.md), we explored SiYuan's powerful query system. Now let's look at how to extend SiYuan's functionality through its plugin architecture. The plugin system allows developers to add new features, integrate with external services, and customize the user experience without modifying SiYuan's core codebase.
 
 ## Plugin System Overview
@@ -921,3 +924,49 @@ Now that you can extend SiYuan with plugins, let's explore the synchronization s
 ---
 
 *Built with insights from the [SiYuan](https://github.com/siyuan-note/siyuan) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `void`, `menu`, `Plugin` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Plugin Architecture` as an operating subsystem inside **SiYuan Tutorial: Privacy-First Knowledge Management**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Promise`, `plugin`, `json` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Plugin Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `void`.
+2. **Input normalization**: shape incoming data so `menu` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Plugin`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/siyuan-note/siyuan)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `void` and `menu` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Query System & Search](04-query-system.md)
+- [Next Chapter: Chapter 6: Synchronization & Backup](06-synchronization.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/siyuan-tutorial/06-synchronization.md b/tutorials/siyuan-tutorial/06-synchronization.md
index b1aec55f..57efc728 100644
--- a/tutorials/siyuan-tutorial/06-synchronization.md
+++ b/tutorials/siyuan-tutorial/06-synchronization.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Synchronization & Backup
 
+Welcome to **Chapter 6: Synchronization & Backup**. In this part of **SiYuan Tutorial: Privacy-First Knowledge Management**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 5](05-plugin-architecture.md), we explored SiYuan's plugin system for extending functionality. Now let's take a deep dive into one of SiYuan's most critical systems: data synchronization and backup. For a privacy-first application, syncing data securely across devices without relying on proprietary cloud services is a significant engineering challenge.
 
 ## Sync Architecture
@@ -983,3 +986,49 @@ With sync and backup covered, let's explore SiYuan's advanced features. In [Chap
 ---
 
 *Built with insights from the [SiYuan](https://github.com/siyuan-note/siyuan) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `json`, `Kernel`, `snapshot` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Synchronization & Backup` as an operating subsystem inside **SiYuan Tutorial: Privacy-First Knowledge Management**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `result`, `backup`, `cloud` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Synchronization & Backup` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `json`.
+2. **Input normalization**: shape incoming data so `Kernel` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `snapshot`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/siyuan-note/siyuan)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `json` and `Kernel` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Plugin Architecture](05-plugin-architecture.md)
+- [Next Chapter: Chapter 7: Advanced Features](07-advanced-features.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/siyuan-tutorial/07-advanced-features.md b/tutorials/siyuan-tutorial/07-advanced-features.md
index 59f20158..b18c85c5 100644
--- a/tutorials/siyuan-tutorial/07-advanced-features.md
+++ b/tutorials/siyuan-tutorial/07-advanced-features.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Advanced Features
 
+Welcome to **Chapter 7: Advanced Features**. In this part of **SiYuan Tutorial: Privacy-First Knowledge Management**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 6](06-synchronization.md), we covered synchronization and backup strategies. Now let's explore SiYuan's advanced features: templates with Sprig functions, custom widgets, theme development, and the customization capabilities that make SiYuan a truly flexible knowledge management platform.
 
 ## Templates
@@ -1022,3 +1025,49 @@ We've covered SiYuan's customization capabilities. In the final chapter, [Chapte
 ---
 
 *Built with insights from the [SiYuan](https://github.com/siyuan-note/siyuan) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `theme`, `surface`, `protyle` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Advanced Features` as an operating subsystem inside **SiYuan Tutorial: Privacy-First Knowledge Management**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `content`, `primary`, `color` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Advanced Features` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `theme`.
+2. **Input normalization**: shape incoming data so `surface` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `protyle`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/siyuan-note/siyuan)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `theme` and `surface` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Synchronization & Backup](06-synchronization.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/siyuan-tutorial/08-production-deployment.md b/tutorials/siyuan-tutorial/08-production-deployment.md
index d4985130..4474c088 100644
--- a/tutorials/siyuan-tutorial/08-production-deployment.md
+++ b/tutorials/siyuan-tutorial/08-production-deployment.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **SiYuan Tutorial: Privacy-First Knowledge Management**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 7](07-advanced-features.md), we explored templates, widgets, themes, and API automation. In this final chapter, we'll cover everything you need to deploy SiYuan in production: Docker self-hosting, reverse proxy configuration, security hardening, data migration, monitoring, and enterprise deployment patterns.
 
 ## Deployment Architecture
@@ -1223,3 +1226,48 @@ You now have a comprehensive understanding of how SiYuan works from the ground u
 ---
 
 *Built with insights from the [SiYuan](https://github.com/siyuan-note/siyuan) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `siyuan`, `json`, `http` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **SiYuan Tutorial: Privacy-First Knowledge Management**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `config`, `SiYuan`, `backup` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `siyuan`.
+2. **Input normalization**: shape incoming data so `json` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `http`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/siyuan-note/siyuan)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `siyuan` and `json` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Advanced Features](07-advanced-features.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/smolagents-tutorial/01-getting-started.md b/tutorials/smolagents-tutorial/01-getting-started.md
index 19efab70..a7d5bba2 100644
--- a/tutorials/smolagents-tutorial/01-getting-started.md
+++ b/tutorials/smolagents-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: Smolagents Tutorial
 
 # Chapter 1: Getting Started with Smolagents
 
+Welcome to **Chapter 1: Getting Started with Smolagents**. In this part of **Smolagents Tutorial: Hugging Face's Lightweight Agent Framework**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Install smolagents, configure your model backend, and run your first lightweight AI agent in minutes.
 
 ## What is Smolagents?
@@ -391,3 +394,50 @@ In **[Chapter 2: Understanding Agents](02-understanding-agents.md)**, you will e
 ---
 
 *Built with insights from the [Smolagents](https://github.com/huggingface/smolagents) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `smolagents`, `model`, `agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Smolagents` as an operating subsystem inside **Smolagents Tutorial: Hugging Face's Lightweight Agent Framework**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `print`, `CodeAgent`, `HfApiModel` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Smolagents` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `smolagents`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/huggingface/smolagents)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `smolagents` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Understanding Smolagents](02-understanding-agents.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/smolagents-tutorial/02-understanding-agents.md b/tutorials/smolagents-tutorial/02-understanding-agents.md
index 262c9b18..49a575a3 100644
--- a/tutorials/smolagents-tutorial/02-understanding-agents.md
+++ b/tutorials/smolagents-tutorial/02-understanding-agents.md
@@ -8,6 +8,9 @@ parent: Smolagents Tutorial
 
 # Chapter 2: Understanding Smolagents
 
+Welcome to **Chapter 2: Understanding Smolagents**. In this part of **Smolagents Tutorial: Hugging Face's Lightweight Agent Framework**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Compare agent types, understand the execution loop, explore configuration options, and choose the right agent architecture for your task.
 
 ## The Smolagents Architecture
@@ -472,3 +475,51 @@ In **[Chapter 3: Tools & Functions](03-tools.md)**, you will learn how to build
 ---
 
 *Built with insights from the [Smolagents](https://github.com/huggingface/smolagents) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `agent`, `CodeAgent`, `smolagents` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Understanding Smolagents` as an operating subsystem inside **Smolagents Tutorial: Hugging Face's Lightweight Agent Framework**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `tools`, `model`, `HfApiModel` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Understanding Smolagents` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `agent`.
+2. **Input normalization**: shape incoming data so `CodeAgent` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `smolagents`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/huggingface/smolagents)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `agent` and `CodeAgent` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Smolagents](01-getting-started.md)
+- [Next Chapter: Chapter 3: Tools & Functions](03-tools.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/smolagents-tutorial/03-tools.md b/tutorials/smolagents-tutorial/03-tools.md
index 58394805..28d84403 100644
--- a/tutorials/smolagents-tutorial/03-tools.md
+++ b/tutorials/smolagents-tutorial/03-tools.md
@@ -8,6 +8,9 @@ parent: Smolagents Tutorial
 
 # Chapter 3: Tools & Functions
 
+Welcome to **Chapter 3: Tools & Functions**. In this part of **Smolagents Tutorial: Hugging Face's Lightweight Agent Framework**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Build custom tools with the `@tool` decorator, use built-in tools, design effective tool APIs, and compose tools for complex workflows.
 
 ## How Tools Work in Smolagents
@@ -606,3 +609,51 @@ In **[Chapter 4: Code Execution](04-code-execution.md)**, you will learn how smo
 ---
 
 *Built with insights from the [Smolagents](https://github.com/huggingface/smolagents) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `result`, `tool`, `agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Tools & Functions` as an operating subsystem inside **Smolagents Tutorial: Hugging Face's Lightweight Agent Framework**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `smolagents`, `CodeAgent`, `List` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Tools & Functions` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `result`.
+2. **Input normalization**: shape incoming data so `tool` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/huggingface/smolagents)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `result` and `tool` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Understanding Smolagents](02-understanding-agents.md)
+- [Next Chapter: Chapter 4: Safe Code Execution](04-code-execution.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/smolagents-tutorial/04-code-execution.md b/tutorials/smolagents-tutorial/04-code-execution.md
index 633adb27..dd2d5887 100644
--- a/tutorials/smolagents-tutorial/04-code-execution.md
+++ b/tutorials/smolagents-tutorial/04-code-execution.md
@@ -8,6 +8,9 @@ parent: Smolagents Tutorial
 
 # Chapter 4: Safe Code Execution
 
+Welcome to **Chapter 4: Safe Code Execution**. In this part of **Smolagents Tutorial: Hugging Face's Lightweight Agent Framework**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Understand how smolagents executes agent-generated Python code, configure the sandbox, manage import restrictions, handle errors, and build observable execution pipelines.
 
 ## How Code Execution Works
@@ -563,3 +566,51 @@ In **[Chapter 5: Multi-Step Reasoning](05-multi-step.md)**, you will learn how t
 ---
 
 *Built with insights from the [Smolagents](https://github.com/huggingface/smolagents) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `agent`, `result`, `CodeAgent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Safe Code Execution` as an operating subsystem inside **Smolagents Tutorial: Hugging Face's Lightweight Agent Framework**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `HfApiModel`, `max_steps`, `tools` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Safe Code Execution` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `agent`.
+2. **Input normalization**: shape incoming data so `result` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `CodeAgent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/huggingface/smolagents)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `agent` and `result` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Tools & Functions](03-tools.md)
+- [Next Chapter: Chapter 5: Multi-Step Reasoning](05-multi-step.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/smolagents-tutorial/05-multi-step.md b/tutorials/smolagents-tutorial/05-multi-step.md
index aaa1d1ce..0589e5cc 100644
--- a/tutorials/smolagents-tutorial/05-multi-step.md
+++ b/tutorials/smolagents-tutorial/05-multi-step.md
@@ -8,6 +8,9 @@ parent: Smolagents Tutorial
 
 # Chapter 5: Multi-Step Reasoning
 
+Welcome to **Chapter 5: Multi-Step Reasoning**. In this part of **Smolagents Tutorial: Hugging Face's Lightweight Agent Framework**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Structure complex tasks into manageable steps, guide the agent's planning, audit reasoning traces, prevent drift, and build reliable multi-step pipelines.
 
 ## Why Multi-Step Reasoning Matters
@@ -611,3 +614,51 @@ In **[Chapter 6: Memory & Context](06-memory.md)**, you will learn how to manage
 ---
 
 *Built with insights from the [Smolagents](https://github.com/huggingface/smolagents) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `agent`, `result`, `CodeAgent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Multi-Step Reasoning` as an operating subsystem inside **Smolagents Tutorial: Hugging Face's Lightweight Agent Framework**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `HfApiModel`, `tools`, `model` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Multi-Step Reasoning` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `agent`.
+2. **Input normalization**: shape incoming data so `result` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `CodeAgent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/huggingface/smolagents)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `agent` and `result` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Safe Code Execution](04-code-execution.md)
+- [Next Chapter: Chapter 6: Memory & Context](06-memory.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/smolagents-tutorial/06-memory.md b/tutorials/smolagents-tutorial/06-memory.md
index f68f54c8..1d66b85e 100644
--- a/tutorials/smolagents-tutorial/06-memory.md
+++ b/tutorials/smolagents-tutorial/06-memory.md
@@ -8,6 +8,9 @@ parent: Smolagents Tutorial
 
 # Chapter 6: Memory & Context
 
+Welcome to **Chapter 6: Memory & Context**. In this part of **Smolagents Tutorial: Hugging Face's Lightweight Agent Framework**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Manage conversation history, implement RAG-based knowledge retrieval, use tools as memory interfaces, and keep agent context clean and token-efficient.
 
 ## Memory in Smolagents
@@ -651,3 +654,51 @@ In **[Chapter 7: Advanced Patterns](07-advanced.md)**, you will explore multi-ag
 ---
 
 *Built with insights from the [Smolagents](https://github.com/huggingface/smolagents) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `agent`, `turns` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Memory & Context` as an operating subsystem inside **Smolagents Tutorial: Hugging Face's Lightweight Agent Framework**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `context`, `content`, `role` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Memory & Context` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `agent` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `turns`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/huggingface/smolagents)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `agent` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Multi-Step Reasoning](05-multi-step.md)
+- [Next Chapter: Chapter 7: Advanced Patterns](07-advanced.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/smolagents-tutorial/07-advanced.md b/tutorials/smolagents-tutorial/07-advanced.md
index feb8e057..02dd511e 100644
--- a/tutorials/smolagents-tutorial/07-advanced.md
+++ b/tutorials/smolagents-tutorial/07-advanced.md
@@ -8,6 +8,9 @@ parent: Smolagents Tutorial
 
 # Chapter 7: Advanced Patterns
 
+Welcome to **Chapter 7: Advanced Patterns**. In this part of **Smolagents Tutorial: Hugging Face's Lightweight Agent Framework**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Build multi-agent systems, implement router and managed agent patterns, add safety layers, create evaluation frameworks, and orchestrate complex agent workflows.
 
 ## Multi-Agent Architecture Overview
@@ -754,3 +757,51 @@ In **[Chapter 8: Production Deployment](08-production.md)**, you will learn how
 ---
 
 *Built with insights from the [Smolagents](https://github.com/huggingface/smolagents) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `agent`, `model`, `results` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Advanced Patterns` as an operating subsystem inside **Smolagents Tutorial: Hugging Face's Lightweight Agent Framework**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `CodeAgent`, `result`, `task` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Advanced Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `agent`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `results`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/huggingface/smolagents)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `agent` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Memory & Context](06-memory.md)
+- [Next Chapter: Chapter 8: Production Deployment & Operations](08-production.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/smolagents-tutorial/08-production.md b/tutorials/smolagents-tutorial/08-production.md
index 231165c6..6172d40c 100644
--- a/tutorials/smolagents-tutorial/08-production.md
+++ b/tutorials/smolagents-tutorial/08-production.md
@@ -8,6 +8,9 @@ parent: Smolagents Tutorial
 
 # Chapter 8: Production Deployment & Operations
 
+Welcome to **Chapter 8: Production Deployment & Operations**. In this part of **Smolagents Tutorial: Hugging Face's Lightweight Agent Framework**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy smolagents-powered services with robust APIs, monitoring, scaling strategies, cost management, and operational best practices.
 
 ## Production Architecture
@@ -877,3 +880,50 @@ Deploying smolagents in production requires attention to API design, authenticat
 ---
 
 *Built with insights from the [Smolagents](https://github.com/huggingface/smolagents) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `agent`, `model`, `request_id` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment & Operations` as an operating subsystem inside **Smolagents Tutorial: Hugging Face's Lightweight Agent Framework**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `max_steps`, `result`, `smolagents` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment & Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `agent`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `request_id`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/huggingface/smolagents)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `agent` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Advanced Patterns](07-advanced.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/stagewise-tutorial/01-getting-started-and-cli-bootstrap.md b/tutorials/stagewise-tutorial/01-getting-started-and-cli-bootstrap.md
index 4c0191c6..ac19bb1f 100644
--- a/tutorials/stagewise-tutorial/01-getting-started-and-cli-bootstrap.md
+++ b/tutorials/stagewise-tutorial/01-getting-started-and-cli-bootstrap.md
@@ -7,6 +7,9 @@ parent: Stagewise Tutorial
 
 # Chapter 1: Getting Started and CLI Bootstrap
 
+Welcome to **Chapter 1: Getting Started and CLI Bootstrap**. In this part of **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Stagewise running with the correct workspace assumptions so the agent can safely edit your frontend codebase.
 
 ## Learning Goals
@@ -45,3 +48,598 @@ pnpm dlx stagewise@latest
 You now have a working Stagewise baseline and understand the root-directory requirement.
 
 Next: [Chapter 2: Proxy and Toolbar Architecture](02-proxy-and-toolbar-architecture.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- tutorial slug: **stagewise-tutorial**
+- chapter focus: **Chapter 1: Getting Started and CLI Bootstrap**
+- system context: **Stagewise Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started and CLI Bootstrap`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Stagewise Repository](https://github.com/stagewise-io/stagewise)
+- [Root README](https://github.com/stagewise-io/stagewise/blob/main/README.md)
+- [Docs Home](https://stagewise.io/docs)
+- [CLI Deep Dive](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/cli-deep-dive.mdx)
+- [Use Different Agents](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/use-different-agents.mdx)
+- [Install Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/install-plugins.mdx)
+- [Build Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-plugins.mdx)
+- [Build Custom Agent Integrations](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-custom-agent-integrations.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Tabby Tutorial](../tabby-tutorial/)
+- [Sweep Tutorial](../sweep-tutorial/)
+- [VibeSDK Tutorial](../vibesdk-tutorial/)
+- [Chapter 1: Getting Started and CLI Bootstrap](01-getting-started-and-cli-bootstrap.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started and CLI Bootstrap`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started and CLI Bootstrap
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `stagewise`, `latest`, `your` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started and CLI Bootstrap` as an operating subsystem inside **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `frontend`, `root`, `where` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started and CLI Bootstrap` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `stagewise`.
+2. **Input normalization**: shape incoming data so `latest` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `your`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Stagewise Repository](https://github.com/stagewise-io/stagewise)
+  Why it matters: authoritative reference on `Stagewise Repository` (github.com).
+- [Root README](https://github.com/stagewise-io/stagewise/blob/main/README.md)
+  Why it matters: authoritative reference on `Root README` (github.com).
+- [Docs Home](https://stagewise.io/docs)
+  Why it matters: authoritative reference on `Docs Home` (stagewise.io).
+- [CLI Deep Dive](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/cli-deep-dive.mdx)
+  Why it matters: authoritative reference on `CLI Deep Dive` (github.com).
+- [Use Different Agents](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/use-different-agents.mdx)
+  Why it matters: authoritative reference on `Use Different Agents` (github.com).
+- [Install Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/install-plugins.mdx)
+  Why it matters: authoritative reference on `Install Plugins` (github.com).
+- [Build Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-plugins.mdx)
+  Why it matters: authoritative reference on `Build Plugins` (github.com).
+- [Build Custom Agent Integrations](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-custom-agent-integrations.mdx)
+  Why it matters: authoritative reference on `Build Custom Agent Integrations` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `stagewise` and `latest` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Proxy and Toolbar Architecture](02-proxy-and-toolbar-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/stagewise-tutorial/02-proxy-and-toolbar-architecture.md b/tutorials/stagewise-tutorial/02-proxy-and-toolbar-architecture.md
index 9d1cce1c..3a3d6397 100644
--- a/tutorials/stagewise-tutorial/02-proxy-and-toolbar-architecture.md
+++ b/tutorials/stagewise-tutorial/02-proxy-and-toolbar-architecture.md
@@ -7,6 +7,9 @@ parent: Stagewise Tutorial
 
 # Chapter 2: Proxy and Toolbar Architecture
 
+Welcome to **Chapter 2: Proxy and Toolbar Architecture**. In this part of **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Stagewise works by proxying your app and injecting a toolbar layer that captures UI context for coding-agent prompts.
 
 ## Learning Goals
@@ -50,3 +53,599 @@ sequenceDiagram
 You now understand how Stagewise integrates without replacing your existing dev server workflow.
 
 Next: [Chapter 3: Bridge Mode and Multi-Agent Integrations](03-bridge-mode-and-multi-agent-integrations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- tutorial slug: **stagewise-tutorial**
+- chapter focus: **Chapter 2: Proxy and Toolbar Architecture**
+- system context: **Stagewise Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Proxy and Toolbar Architecture`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Stagewise Repository](https://github.com/stagewise-io/stagewise)
+- [Root README](https://github.com/stagewise-io/stagewise/blob/main/README.md)
+- [Docs Home](https://stagewise.io/docs)
+- [CLI Deep Dive](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/cli-deep-dive.mdx)
+- [Use Different Agents](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/use-different-agents.mdx)
+- [Install Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/install-plugins.mdx)
+- [Build Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-plugins.mdx)
+- [Build Custom Agent Integrations](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-custom-agent-integrations.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Tabby Tutorial](../tabby-tutorial/)
+- [Sweep Tutorial](../sweep-tutorial/)
+- [VibeSDK Tutorial](../vibesdk-tutorial/)
+- [Chapter 1: Getting Started and CLI Bootstrap](01-getting-started-and-cli-bootstrap.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Proxy and Toolbar Architecture`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Proxy and Toolbar Architecture
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Proxy`, `Browser`, `participant` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Proxy and Toolbar Architecture` as an operating subsystem inside **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Agent`, `sequenceDiagram`, `Stagewise` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Proxy and Toolbar Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Proxy`.
+2. **Input normalization**: shape incoming data so `Browser` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `participant`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Stagewise Repository](https://github.com/stagewise-io/stagewise)
+  Why it matters: authoritative reference on `Stagewise Repository` (github.com).
+- [Root README](https://github.com/stagewise-io/stagewise/blob/main/README.md)
+  Why it matters: authoritative reference on `Root README` (github.com).
+- [Docs Home](https://stagewise.io/docs)
+  Why it matters: authoritative reference on `Docs Home` (stagewise.io).
+- [CLI Deep Dive](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/cli-deep-dive.mdx)
+  Why it matters: authoritative reference on `CLI Deep Dive` (github.com).
+- [Use Different Agents](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/use-different-agents.mdx)
+  Why it matters: authoritative reference on `Use Different Agents` (github.com).
+- [Install Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/install-plugins.mdx)
+  Why it matters: authoritative reference on `Install Plugins` (github.com).
+- [Build Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-plugins.mdx)
+  Why it matters: authoritative reference on `Build Plugins` (github.com).
+- [Build Custom Agent Integrations](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-custom-agent-integrations.mdx)
+  Why it matters: authoritative reference on `Build Custom Agent Integrations` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Proxy` and `Browser` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started and CLI Bootstrap](01-getting-started-and-cli-bootstrap.md)
+- [Next Chapter: Chapter 3: Bridge Mode and Multi-Agent Integrations](03-bridge-mode-and-multi-agent-integrations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/stagewise-tutorial/03-bridge-mode-and-multi-agent-integrations.md b/tutorials/stagewise-tutorial/03-bridge-mode-and-multi-agent-integrations.md
index 6cd3786b..0e7df8a1 100644
--- a/tutorials/stagewise-tutorial/03-bridge-mode-and-multi-agent-integrations.md
+++ b/tutorials/stagewise-tutorial/03-bridge-mode-and-multi-agent-integrations.md
@@ -7,6 +7,9 @@ parent: Stagewise Tutorial
 
 # Chapter 3: Bridge Mode and Multi-Agent Integrations
 
+Welcome to **Chapter 3: Bridge Mode and Multi-Agent Integrations**. In this part of **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Bridge mode allows Stagewise to route prompts to external IDE agents instead of the built-in Stagewise agent runtime.
 
 ## Learning Goals
@@ -46,3 +49,599 @@ stagewise -b -w ~/repos/my-dev-app
 You now know how to route Stagewise browser context into external coding-agent ecosystems.
 
 Next: [Chapter 4: Configuration and Plugin Loading](04-configuration-and-plugin-loading.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- tutorial slug: **stagewise-tutorial**
+- chapter focus: **Chapter 3: Bridge Mode and Multi-Agent Integrations**
+- system context: **Stagewise Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Bridge Mode and Multi-Agent Integrations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Stagewise Repository](https://github.com/stagewise-io/stagewise)
+- [Root README](https://github.com/stagewise-io/stagewise/blob/main/README.md)
+- [Docs Home](https://stagewise.io/docs)
+- [CLI Deep Dive](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/cli-deep-dive.mdx)
+- [Use Different Agents](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/use-different-agents.mdx)
+- [Install Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/install-plugins.mdx)
+- [Build Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-plugins.mdx)
+- [Build Custom Agent Integrations](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-custom-agent-integrations.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Tabby Tutorial](../tabby-tutorial/)
+- [Sweep Tutorial](../sweep-tutorial/)
+- [VibeSDK Tutorial](../vibesdk-tutorial/)
+- [Chapter 1: Getting Started and CLI Bootstrap](01-getting-started-and-cli-bootstrap.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Bridge Mode and Multi-Agent Integrations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Bridge Mode and Multi-Agent Integrations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `stagewise`, `repos` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Bridge Mode and Multi-Agent Integrations` as an operating subsystem inside **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Bridge Mode and Multi-Agent Integrations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `stagewise`.
+2. **Input normalization**: shape incoming data so `repos` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Stagewise Repository](https://github.com/stagewise-io/stagewise)
+  Why it matters: authoritative reference on `Stagewise Repository` (github.com).
+- [Root README](https://github.com/stagewise-io/stagewise/blob/main/README.md)
+  Why it matters: authoritative reference on `Root README` (github.com).
+- [Docs Home](https://stagewise.io/docs)
+  Why it matters: authoritative reference on `Docs Home` (stagewise.io).
+- [CLI Deep Dive](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/cli-deep-dive.mdx)
+  Why it matters: authoritative reference on `CLI Deep Dive` (github.com).
+- [Use Different Agents](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/use-different-agents.mdx)
+  Why it matters: authoritative reference on `Use Different Agents` (github.com).
+- [Install Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/install-plugins.mdx)
+  Why it matters: authoritative reference on `Install Plugins` (github.com).
+- [Build Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-plugins.mdx)
+  Why it matters: authoritative reference on `Build Plugins` (github.com).
+- [Build Custom Agent Integrations](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-custom-agent-integrations.mdx)
+  Why it matters: authoritative reference on `Build Custom Agent Integrations` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `stagewise` and `repos` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Proxy and Toolbar Architecture](02-proxy-and-toolbar-architecture.md)
+- [Next Chapter: Chapter 4: Configuration and Plugin Loading](04-configuration-and-plugin-loading.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/stagewise-tutorial/04-configuration-and-plugin-loading.md b/tutorials/stagewise-tutorial/04-configuration-and-plugin-loading.md
index fe885658..208ba6ce 100644
--- a/tutorials/stagewise-tutorial/04-configuration-and-plugin-loading.md
+++ b/tutorials/stagewise-tutorial/04-configuration-and-plugin-loading.md
@@ -7,6 +7,9 @@ parent: Stagewise Tutorial
 
 # Chapter 4: Configuration and Plugin Loading
 
+Welcome to **Chapter 4: Configuration and Plugin Loading**. In this part of **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 `stagewise.json` governs ports, workspace behavior, and plugin loading strategy.
 
 ## Learning Goals
@@ -48,3 +51,599 @@ parent: Stagewise Tutorial
 You now have a configuration model for predictable per-project Stagewise behavior.
 
 Next: [Chapter 5: Building Plugins with Plugin SDK](05-building-plugins-with-plugin-sdk.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- tutorial slug: **stagewise-tutorial**
+- chapter focus: **Chapter 4: Configuration and Plugin Loading**
+- system context: **Stagewise Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Configuration and Plugin Loading`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Stagewise Repository](https://github.com/stagewise-io/stagewise)
+- [Root README](https://github.com/stagewise-io/stagewise/blob/main/README.md)
+- [Docs Home](https://stagewise.io/docs)
+- [CLI Deep Dive](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/cli-deep-dive.mdx)
+- [Use Different Agents](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/use-different-agents.mdx)
+- [Install Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/install-plugins.mdx)
+- [Build Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-plugins.mdx)
+- [Build Custom Agent Integrations](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-custom-agent-integrations.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Tabby Tutorial](../tabby-tutorial/)
+- [Sweep Tutorial](../sweep-tutorial/)
+- [VibeSDK Tutorial](../vibesdk-tutorial/)
+- [Chapter 1: Getting Started and CLI Bootstrap](01-getting-started-and-cli-bootstrap.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Configuration and Plugin Loading`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Configuration and Plugin Loading
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `plugin`, `plugins`, `custom` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Configuration and Plugin Loading` as an operating subsystem inside **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `port`, `appPort`, `autoPlugins` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Configuration and Plugin Loading` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `plugin`.
+2. **Input normalization**: shape incoming data so `plugins` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `custom`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Stagewise Repository](https://github.com/stagewise-io/stagewise)
+  Why it matters: authoritative reference on `Stagewise Repository` (github.com).
+- [Root README](https://github.com/stagewise-io/stagewise/blob/main/README.md)
+  Why it matters: authoritative reference on `Root README` (github.com).
+- [Docs Home](https://stagewise.io/docs)
+  Why it matters: authoritative reference on `Docs Home` (stagewise.io).
+- [CLI Deep Dive](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/cli-deep-dive.mdx)
+  Why it matters: authoritative reference on `CLI Deep Dive` (github.com).
+- [Use Different Agents](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/use-different-agents.mdx)
+  Why it matters: authoritative reference on `Use Different Agents` (github.com).
+- [Install Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/install-plugins.mdx)
+  Why it matters: authoritative reference on `Install Plugins` (github.com).
+- [Build Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-plugins.mdx)
+  Why it matters: authoritative reference on `Build Plugins` (github.com).
+- [Build Custom Agent Integrations](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-custom-agent-integrations.mdx)
+  Why it matters: authoritative reference on `Build Custom Agent Integrations` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `plugin` and `plugins` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Bridge Mode and Multi-Agent Integrations](03-bridge-mode-and-multi-agent-integrations.md)
+- [Next Chapter: Chapter 5: Building Plugins with Plugin SDK](05-building-plugins-with-plugin-sdk.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/stagewise-tutorial/05-building-plugins-with-plugin-sdk.md b/tutorials/stagewise-tutorial/05-building-plugins-with-plugin-sdk.md
index aa106735..1fa2ce87 100644
--- a/tutorials/stagewise-tutorial/05-building-plugins-with-plugin-sdk.md
+++ b/tutorials/stagewise-tutorial/05-building-plugins-with-plugin-sdk.md
@@ -7,6 +7,9 @@ parent: Stagewise Tutorial
 
 # Chapter 5: Building Plugins with Plugin SDK
 
+Welcome to **Chapter 5: Building Plugins with Plugin SDK**. In this part of **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Plugins let teams add custom toolbar UX and prompt behavior without forking the core project.
 
 ## Learning Goals
@@ -52,3 +55,599 @@ export default MyPlugin;
 You now know how to create and iterate on custom Stagewise plugins.
 
 Next: [Chapter 6: Custom Agent Integrations with Agent Interface](06-custom-agent-integrations-with-agent-interface.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- tutorial slug: **stagewise-tutorial**
+- chapter focus: **Chapter 5: Building Plugins with Plugin SDK**
+- system context: **Stagewise Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Building Plugins with Plugin SDK`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Stagewise Repository](https://github.com/stagewise-io/stagewise)
+- [Root README](https://github.com/stagewise-io/stagewise/blob/main/README.md)
+- [Docs Home](https://stagewise.io/docs)
+- [CLI Deep Dive](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/cli-deep-dive.mdx)
+- [Use Different Agents](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/use-different-agents.mdx)
+- [Install Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/install-plugins.mdx)
+- [Build Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-plugins.mdx)
+- [Build Custom Agent Integrations](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-custom-agent-integrations.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Tabby Tutorial](../tabby-tutorial/)
+- [Sweep Tutorial](../sweep-tutorial/)
+- [VibeSDK Tutorial](../vibesdk-tutorial/)
+- [Chapter 1: Getting Started and CLI Bootstrap](01-getting-started-and-cli-bootstrap.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Building Plugins with Plugin SDK`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Building Plugins with Plugin SDK
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `stagewise`, `plugin`, `ToolbarPlugin` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Building Plugins with Plugin SDK` as an operating subsystem inside **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `toolbar`, `MyPlugin`, `create` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Building Plugins with Plugin SDK` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `stagewise`.
+2. **Input normalization**: shape incoming data so `plugin` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `ToolbarPlugin`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Stagewise Repository](https://github.com/stagewise-io/stagewise)
+  Why it matters: authoritative reference on `Stagewise Repository` (github.com).
+- [Root README](https://github.com/stagewise-io/stagewise/blob/main/README.md)
+  Why it matters: authoritative reference on `Root README` (github.com).
+- [Docs Home](https://stagewise.io/docs)
+  Why it matters: authoritative reference on `Docs Home` (stagewise.io).
+- [CLI Deep Dive](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/cli-deep-dive.mdx)
+  Why it matters: authoritative reference on `CLI Deep Dive` (github.com).
+- [Use Different Agents](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/use-different-agents.mdx)
+  Why it matters: authoritative reference on `Use Different Agents` (github.com).
+- [Install Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/install-plugins.mdx)
+  Why it matters: authoritative reference on `Install Plugins` (github.com).
+- [Build Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-plugins.mdx)
+  Why it matters: authoritative reference on `Build Plugins` (github.com).
+- [Build Custom Agent Integrations](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-custom-agent-integrations.mdx)
+  Why it matters: authoritative reference on `Build Custom Agent Integrations` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `stagewise` and `plugin` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Configuration and Plugin Loading](04-configuration-and-plugin-loading.md)
+- [Next Chapter: Chapter 6: Custom Agent Integrations with Agent Interface](06-custom-agent-integrations-with-agent-interface.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/stagewise-tutorial/06-custom-agent-integrations-with-agent-interface.md b/tutorials/stagewise-tutorial/06-custom-agent-integrations-with-agent-interface.md
index cd315dc1..af5a98e6 100644
--- a/tutorials/stagewise-tutorial/06-custom-agent-integrations-with-agent-interface.md
+++ b/tutorials/stagewise-tutorial/06-custom-agent-integrations-with-agent-interface.md
@@ -7,6 +7,9 @@ parent: Stagewise Tutorial
 
 # Chapter 6: Custom Agent Integrations with Agent Interface
 
+Welcome to **Chapter 6: Custom Agent Integrations with Agent Interface**. In this part of **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Stagewise provides a dedicated interface for wiring custom agents while keeping toolbar protocol behavior stable.
 
 ## Learning Goals
@@ -43,3 +46,599 @@ server.interface.availability.set(true);
 You now have an implementation map for connecting custom agents into Stagewise workflows.
 
 Next: [Chapter 7: Troubleshooting, Security, and Operations](07-troubleshooting-security-and-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- tutorial slug: **stagewise-tutorial**
+- chapter focus: **Chapter 6: Custom Agent Integrations with Agent Interface**
+- system context: **Stagewise Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Custom Agent Integrations with Agent Interface`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Stagewise Repository](https://github.com/stagewise-io/stagewise)
+- [Root README](https://github.com/stagewise-io/stagewise/blob/main/README.md)
+- [Docs Home](https://stagewise.io/docs)
+- [CLI Deep Dive](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/cli-deep-dive.mdx)
+- [Use Different Agents](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/use-different-agents.mdx)
+- [Install Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/install-plugins.mdx)
+- [Build Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-plugins.mdx)
+- [Build Custom Agent Integrations](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-custom-agent-integrations.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Tabby Tutorial](../tabby-tutorial/)
+- [Sweep Tutorial](../sweep-tutorial/)
+- [VibeSDK Tutorial](../vibesdk-tutorial/)
+- [Chapter 1: Getting Started and CLI Bootstrap](01-getting-started-and-cli-bootstrap.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Custom Agent Integrations with Agent Interface`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Custom Agent Integrations with Agent Interface
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `createAgentServer`, `agent`, `interface` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Custom Agent Integrations with Agent Interface` as an operating subsystem inside **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `server`, `stagewise`, `availability` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Custom Agent Integrations with Agent Interface` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `createAgentServer`.
+2. **Input normalization**: shape incoming data so `agent` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `interface`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Stagewise Repository](https://github.com/stagewise-io/stagewise)
+  Why it matters: authoritative reference on `Stagewise Repository` (github.com).
+- [Root README](https://github.com/stagewise-io/stagewise/blob/main/README.md)
+  Why it matters: authoritative reference on `Root README` (github.com).
+- [Docs Home](https://stagewise.io/docs)
+  Why it matters: authoritative reference on `Docs Home` (stagewise.io).
+- [CLI Deep Dive](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/cli-deep-dive.mdx)
+  Why it matters: authoritative reference on `CLI Deep Dive` (github.com).
+- [Use Different Agents](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/use-different-agents.mdx)
+  Why it matters: authoritative reference on `Use Different Agents` (github.com).
+- [Install Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/install-plugins.mdx)
+  Why it matters: authoritative reference on `Install Plugins` (github.com).
+- [Build Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-plugins.mdx)
+  Why it matters: authoritative reference on `Build Plugins` (github.com).
+- [Build Custom Agent Integrations](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-custom-agent-integrations.mdx)
+  Why it matters: authoritative reference on `Build Custom Agent Integrations` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `createAgentServer` and `agent` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Building Plugins with Plugin SDK](05-building-plugins-with-plugin-sdk.md)
+- [Next Chapter: Chapter 7: Troubleshooting, Security, and Operations](07-troubleshooting-security-and-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/stagewise-tutorial/07-troubleshooting-security-and-operations.md b/tutorials/stagewise-tutorial/07-troubleshooting-security-and-operations.md
index 96c4862c..ed2e6158 100644
--- a/tutorials/stagewise-tutorial/07-troubleshooting-security-and-operations.md
+++ b/tutorials/stagewise-tutorial/07-troubleshooting-security-and-operations.md
@@ -7,6 +7,9 @@ parent: Stagewise Tutorial
 
 # Chapter 7: Troubleshooting, Security, and Operations
 
+Welcome to **Chapter 7: Troubleshooting, Security, and Operations**. In this part of **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers practical operational concerns: common runtime failures, security boundaries, and production-minded usage.
 
 ## Learning Goals
@@ -39,3 +42,607 @@ This chapter covers practical operational concerns: common runtime failures, sec
 You now have a troubleshooting and operations baseline for reliable Stagewise sessions.
 
 Next: [Chapter 8: Contribution Workflow and Ecosystem Evolution](08-contribution-workflow-and-ecosystem-evolution.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- tutorial slug: **stagewise-tutorial**
+- chapter focus: **Chapter 7: Troubleshooting, Security, and Operations**
+- system context: **Stagewise Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Troubleshooting, Security, and Operations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Stagewise Repository](https://github.com/stagewise-io/stagewise)
+- [Root README](https://github.com/stagewise-io/stagewise/blob/main/README.md)
+- [Docs Home](https://stagewise.io/docs)
+- [CLI Deep Dive](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/cli-deep-dive.mdx)
+- [Use Different Agents](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/use-different-agents.mdx)
+- [Install Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/install-plugins.mdx)
+- [Build Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-plugins.mdx)
+- [Build Custom Agent Integrations](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-custom-agent-integrations.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Tabby Tutorial](../tabby-tutorial/)
+- [Sweep Tutorial](../sweep-tutorial/)
+- [VibeSDK Tutorial](../vibesdk-tutorial/)
+- [Chapter 1: Getting Started and CLI Bootstrap](01-getting-started-and-cli-bootstrap.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Troubleshooting, Security, and Operations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Troubleshooting, Security, and Operations
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Troubleshooting, Security, and Operations` as an operating subsystem inside **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Troubleshooting, Security, and Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Stagewise Repository](https://github.com/stagewise-io/stagewise)
+  Why it matters: authoritative reference on `Stagewise Repository` (github.com).
+- [Root README](https://github.com/stagewise-io/stagewise/blob/main/README.md)
+  Why it matters: authoritative reference on `Root README` (github.com).
+- [Docs Home](https://stagewise.io/docs)
+  Why it matters: authoritative reference on `Docs Home` (stagewise.io).
+- [CLI Deep Dive](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/cli-deep-dive.mdx)
+  Why it matters: authoritative reference on `CLI Deep Dive` (github.com).
+- [Use Different Agents](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/use-different-agents.mdx)
+  Why it matters: authoritative reference on `Use Different Agents` (github.com).
+- [Install Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/install-plugins.mdx)
+  Why it matters: authoritative reference on `Install Plugins` (github.com).
+- [Build Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-plugins.mdx)
+  Why it matters: authoritative reference on `Build Plugins` (github.com).
+- [Build Custom Agent Integrations](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-custom-agent-integrations.mdx)
+  Why it matters: authoritative reference on `Build Custom Agent Integrations` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Custom Agent Integrations with Agent Interface](06-custom-agent-integrations-with-agent-interface.md)
+- [Next Chapter: Chapter 8: Contribution Workflow and Ecosystem Evolution](08-contribution-workflow-and-ecosystem-evolution.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/stagewise-tutorial/08-contribution-workflow-and-ecosystem-evolution.md b/tutorials/stagewise-tutorial/08-contribution-workflow-and-ecosystem-evolution.md
index 96cf1f4c..85d8555b 100644
--- a/tutorials/stagewise-tutorial/08-contribution-workflow-and-ecosystem-evolution.md
+++ b/tutorials/stagewise-tutorial/08-contribution-workflow-and-ecosystem-evolution.md
@@ -7,6 +7,9 @@ parent: Stagewise Tutorial
 
 # Chapter 8: Contribution Workflow and Ecosystem Evolution
 
+Welcome to **Chapter 8: Contribution Workflow and Ecosystem Evolution**. In this part of **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Stagewise is an active monorepo with clear contribution mechanics and a growing frontend-agent ecosystem.
 
 ## Learning Goals
@@ -45,3 +48,598 @@ pnpm test
 You now have an end-to-end model for adopting, extending, and contributing to Stagewise in production frontend environments.
 
 Next: connect this flow with [VibeSDK](../vibesdk-tutorial/) and [OpenCode](../opencode-tutorial/).
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- tutorial slug: **stagewise-tutorial**
+- chapter focus: **Chapter 8: Contribution Workflow and Ecosystem Evolution**
+- system context: **Stagewise Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Contribution Workflow and Ecosystem Evolution`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Stagewise Repository](https://github.com/stagewise-io/stagewise)
+- [Root README](https://github.com/stagewise-io/stagewise/blob/main/README.md)
+- [Docs Home](https://stagewise.io/docs)
+- [CLI Deep Dive](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/cli-deep-dive.mdx)
+- [Use Different Agents](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/use-different-agents.mdx)
+- [Install Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/install-plugins.mdx)
+- [Build Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-plugins.mdx)
+- [Build Custom Agent Integrations](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-custom-agent-integrations.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Tabby Tutorial](../tabby-tutorial/)
+- [Sweep Tutorial](../sweep-tutorial/)
+- [VibeSDK Tutorial](../vibesdk-tutorial/)
+- [Chapter 1: Getting Started and CLI Bootstrap](01-getting-started-and-cli-bootstrap.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Contribution Workflow and Ecosystem Evolution`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Contribution Workflow and Ecosystem Evolution
+
+- tutorial context: **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `pnpm`, `install`, `build` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Contribution Workflow and Ecosystem Evolution` as an operating subsystem inside **Stagewise Tutorial: Frontend Coding Agent Workflows in Real Browser Context**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `lint`, `test` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Contribution Workflow and Ecosystem Evolution` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `pnpm`.
+2. **Input normalization**: shape incoming data so `install` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `build`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Stagewise Repository](https://github.com/stagewise-io/stagewise)
+  Why it matters: authoritative reference on `Stagewise Repository` (github.com).
+- [Root README](https://github.com/stagewise-io/stagewise/blob/main/README.md)
+  Why it matters: authoritative reference on `Root README` (github.com).
+- [Docs Home](https://stagewise.io/docs)
+  Why it matters: authoritative reference on `Docs Home` (stagewise.io).
+- [CLI Deep Dive](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/cli-deep-dive.mdx)
+  Why it matters: authoritative reference on `CLI Deep Dive` (github.com).
+- [Use Different Agents](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/use-different-agents.mdx)
+  Why it matters: authoritative reference on `Use Different Agents` (github.com).
+- [Install Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/advanced-usage/install-plugins.mdx)
+  Why it matters: authoritative reference on `Install Plugins` (github.com).
+- [Build Plugins](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-plugins.mdx)
+  Why it matters: authoritative reference on `Build Plugins` (github.com).
+- [Build Custom Agent Integrations](https://github.com/stagewise-io/stagewise/blob/main/apps/website/content/docs/developer-guides/build-custom-agent-integrations.mdx)
+  Why it matters: authoritative reference on `Build Custom Agent Integrations` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `pnpm` and `install` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Troubleshooting, Security, and Operations](07-troubleshooting-security-and-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/strands-agents-tutorial/01-getting-started.md b/tutorials/strands-agents-tutorial/01-getting-started.md
index 784829f1..b4c90dab 100644
--- a/tutorials/strands-agents-tutorial/01-getting-started.md
+++ b/tutorials/strands-agents-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Strands Agents Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets a first Strands agent running with minimal setup.
 
 ## Learning Goals
@@ -44,3 +47,601 @@ agent("What is the square root of 1764?")
 You now have Strands installed with a working first invocation.
 
 Next: [Chapter 2: Agent Loop and Model-Driven Architecture](02-agent-loop-and-model-driven-architecture.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- tutorial slug: **strands-agents-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Strands Agents Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Strands Python SDK Repository](https://github.com/strands-agents/sdk-python)
+- [Strands README](https://github.com/strands-agents/sdk-python/blob/main/README.md)
+- [Strands Documentation](https://strandsagents.com/latest/documentation/docs/)
+- [Strands Python Quickstart](https://strandsagents.com/latest/documentation/docs/user-guide/quickstart/python/)
+- [Strands MCP Client Architecture](https://github.com/strands-agents/sdk-python/blob/main/docs/MCP_CLIENT_ARCHITECTURE.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [CrewAI Tutorial](../crewai-tutorial/)
+- [Anything LLM Tutorial](../anything-llm-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `venv`, `strands`, `agents` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `tools`, `Agent`, `calculator` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `venv`.
+2. **Input normalization**: shape incoming data so `strands` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `agents`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Strands Python SDK Repository](https://github.com/strands-agents/sdk-python)
+  Why it matters: authoritative reference on `Strands Python SDK Repository` (github.com).
+- [Strands README](https://github.com/strands-agents/sdk-python/blob/main/README.md)
+  Why it matters: authoritative reference on `Strands README` (github.com).
+- [Strands Documentation](https://strandsagents.com/latest/documentation/docs/)
+  Why it matters: authoritative reference on `Strands Documentation` (strandsagents.com).
+- [Strands Python Quickstart](https://strandsagents.com/latest/documentation/docs/user-guide/quickstart/python/)
+  Why it matters: authoritative reference on `Strands Python Quickstart` (strandsagents.com).
+- [Strands MCP Client Architecture](https://github.com/strands-agents/sdk-python/blob/main/docs/MCP_CLIENT_ARCHITECTURE.md)
+  Why it matters: authoritative reference on `Strands MCP Client Architecture` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `venv` and `strands` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Agent Loop and Model-Driven Architecture](02-agent-loop-and-model-driven-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/strands-agents-tutorial/02-agent-loop-and-model-driven-architecture.md b/tutorials/strands-agents-tutorial/02-agent-loop-and-model-driven-architecture.md
index 0f406594..b663346c 100644
--- a/tutorials/strands-agents-tutorial/02-agent-loop-and-model-driven-architecture.md
+++ b/tutorials/strands-agents-tutorial/02-agent-loop-and-model-driven-architecture.md
@@ -7,6 +7,9 @@ parent: Strands Agents Tutorial
 
 # Chapter 2: Agent Loop and Model-Driven Architecture
 
+Welcome to **Chapter 2: Agent Loop and Model-Driven Architecture**. In this part of **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains why Strands is described as model-driven and how that affects design choices.
 
 ## Learning Goals
@@ -33,3 +36,598 @@ This chapter explains why Strands is described as model-driven and how that affe
 You now have the foundation to design Strands agents with clearer tradeoff awareness.
 
 Next: [Chapter 3: Tools and MCP Integration](03-tools-and-mcp-integration.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- tutorial slug: **strands-agents-tutorial**
+- chapter focus: **Chapter 2: Agent Loop and Model-Driven Architecture**
+- system context: **Strands Agents Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Agent Loop and Model-Driven Architecture`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Strands Python SDK Repository](https://github.com/strands-agents/sdk-python)
+- [Strands README](https://github.com/strands-agents/sdk-python/blob/main/README.md)
+- [Strands Documentation](https://strandsagents.com/latest/documentation/docs/)
+- [Strands Python Quickstart](https://strandsagents.com/latest/documentation/docs/user-guide/quickstart/python/)
+- [Strands MCP Client Architecture](https://github.com/strands-agents/sdk-python/blob/main/docs/MCP_CLIENT_ARCHITECTURE.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [CrewAI Tutorial](../crewai-tutorial/)
+- [Anything LLM Tutorial](../anything-llm-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Agent Loop and Model-Driven Architecture`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Agent Loop and Model-Driven Architecture
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Agent Loop and Model-Driven Architecture` as an operating subsystem inside **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Agent Loop and Model-Driven Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Strands Python SDK Repository](https://github.com/strands-agents/sdk-python)
+  Why it matters: authoritative reference on `Strands Python SDK Repository` (github.com).
+- [Strands README](https://github.com/strands-agents/sdk-python/blob/main/README.md)
+  Why it matters: authoritative reference on `Strands README` (github.com).
+- [Strands Documentation](https://strandsagents.com/latest/documentation/docs/)
+  Why it matters: authoritative reference on `Strands Documentation` (strandsagents.com).
+- [Strands Python Quickstart](https://strandsagents.com/latest/documentation/docs/user-guide/quickstart/python/)
+  Why it matters: authoritative reference on `Strands Python Quickstart` (strandsagents.com).
+- [Strands MCP Client Architecture](https://github.com/strands-agents/sdk-python/blob/main/docs/MCP_CLIENT_ARCHITECTURE.md)
+  Why it matters: authoritative reference on `Strands MCP Client Architecture` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Tools and MCP Integration](03-tools-and-mcp-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/strands-agents-tutorial/03-tools-and-mcp-integration.md b/tutorials/strands-agents-tutorial/03-tools-and-mcp-integration.md
index 37ba9de5..3cdf384d 100644
--- a/tutorials/strands-agents-tutorial/03-tools-and-mcp-integration.md
+++ b/tutorials/strands-agents-tutorial/03-tools-and-mcp-integration.md
@@ -7,6 +7,9 @@ parent: Strands Agents Tutorial
 
 # Chapter 3: Tools and MCP Integration
 
+Welcome to **Chapter 3: Tools and MCP Integration**. In this part of **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers tool composition and MCP usage patterns for real capability expansion.
 
 ## Learning Goals
@@ -37,3 +40,598 @@ Strands runs MCP communication through a background-thread architecture to hide
 You now have practical patterns for integrating tools and MCP safely in Strands.
 
 Next: [Chapter 4: Model Providers and Runtime Strategy](04-model-providers-and-runtime-strategy.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- tutorial slug: **strands-agents-tutorial**
+- chapter focus: **Chapter 3: Tools and MCP Integration**
+- system context: **Strands Agents Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Tools and MCP Integration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Strands Python SDK Repository](https://github.com/strands-agents/sdk-python)
+- [Strands README](https://github.com/strands-agents/sdk-python/blob/main/README.md)
+- [Strands Documentation](https://strandsagents.com/latest/documentation/docs/)
+- [Strands Python Quickstart](https://strandsagents.com/latest/documentation/docs/user-guide/quickstart/python/)
+- [Strands MCP Client Architecture](https://github.com/strands-agents/sdk-python/blob/main/docs/MCP_CLIENT_ARCHITECTURE.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [CrewAI Tutorial](../crewai-tutorial/)
+- [Anything LLM Tutorial](../anything-llm-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Tools and MCP Integration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Tools and MCP Integration
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Tools and MCP Integration` as an operating subsystem inside **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Tools and MCP Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Strands Python SDK Repository](https://github.com/strands-agents/sdk-python)
+  Why it matters: authoritative reference on `Strands Python SDK Repository` (github.com).
+- [Strands README](https://github.com/strands-agents/sdk-python/blob/main/README.md)
+  Why it matters: authoritative reference on `Strands README` (github.com).
+- [Strands Documentation](https://strandsagents.com/latest/documentation/docs/)
+  Why it matters: authoritative reference on `Strands Documentation` (strandsagents.com).
+- [Strands Python Quickstart](https://strandsagents.com/latest/documentation/docs/user-guide/quickstart/python/)
+  Why it matters: authoritative reference on `Strands Python Quickstart` (strandsagents.com).
+- [Strands MCP Client Architecture](https://github.com/strands-agents/sdk-python/blob/main/docs/MCP_CLIENT_ARCHITECTURE.md)
+  Why it matters: authoritative reference on `Strands MCP Client Architecture` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Agent Loop and Model-Driven Architecture](02-agent-loop-and-model-driven-architecture.md)
+- [Next Chapter: Chapter 4: Model Providers and Runtime Strategy](04-model-providers-and-runtime-strategy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/strands-agents-tutorial/04-model-providers-and-runtime-strategy.md b/tutorials/strands-agents-tutorial/04-model-providers-and-runtime-strategy.md
index 027098ca..169b1bda 100644
--- a/tutorials/strands-agents-tutorial/04-model-providers-and-runtime-strategy.md
+++ b/tutorials/strands-agents-tutorial/04-model-providers-and-runtime-strategy.md
@@ -7,6 +7,9 @@ parent: Strands Agents Tutorial
 
 # Chapter 4: Model Providers and Runtime Strategy
 
+Welcome to **Chapter 4: Model Providers and Runtime Strategy**. In this part of **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains provider selection and runtime tuning decisions.
 
 ## Learning Goals
@@ -33,3 +36,598 @@ This chapter explains provider selection and runtime tuning decisions.
 You can now make provider decisions that align with product and operations goals.
 
 Next: [Chapter 5: Hooks, State, and Reliability Controls](05-hooks-state-and-reliability-controls.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- tutorial slug: **strands-agents-tutorial**
+- chapter focus: **Chapter 4: Model Providers and Runtime Strategy**
+- system context: **Strands Agents Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Model Providers and Runtime Strategy`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Strands Python SDK Repository](https://github.com/strands-agents/sdk-python)
+- [Strands README](https://github.com/strands-agents/sdk-python/blob/main/README.md)
+- [Strands Documentation](https://strandsagents.com/latest/documentation/docs/)
+- [Strands Python Quickstart](https://strandsagents.com/latest/documentation/docs/user-guide/quickstart/python/)
+- [Strands MCP Client Architecture](https://github.com/strands-agents/sdk-python/blob/main/docs/MCP_CLIENT_ARCHITECTURE.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [CrewAI Tutorial](../crewai-tutorial/)
+- [Anything LLM Tutorial](../anything-llm-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Model Providers and Runtime Strategy`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Model Providers and Runtime Strategy
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Model Providers and Runtime Strategy` as an operating subsystem inside **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Model Providers and Runtime Strategy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Strands Python SDK Repository](https://github.com/strands-agents/sdk-python)
+  Why it matters: authoritative reference on `Strands Python SDK Repository` (github.com).
+- [Strands README](https://github.com/strands-agents/sdk-python/blob/main/README.md)
+  Why it matters: authoritative reference on `Strands README` (github.com).
+- [Strands Documentation](https://strandsagents.com/latest/documentation/docs/)
+  Why it matters: authoritative reference on `Strands Documentation` (strandsagents.com).
+- [Strands Python Quickstart](https://strandsagents.com/latest/documentation/docs/user-guide/quickstart/python/)
+  Why it matters: authoritative reference on `Strands Python Quickstart` (strandsagents.com).
+- [Strands MCP Client Architecture](https://github.com/strands-agents/sdk-python/blob/main/docs/MCP_CLIENT_ARCHITECTURE.md)
+  Why it matters: authoritative reference on `Strands MCP Client Architecture` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Tools and MCP Integration](03-tools-and-mcp-integration.md)
+- [Next Chapter: Chapter 5: Hooks, State, and Reliability Controls](05-hooks-state-and-reliability-controls.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/strands-agents-tutorial/05-hooks-state-and-reliability-controls.md b/tutorials/strands-agents-tutorial/05-hooks-state-and-reliability-controls.md
index 009c695f..4d5c6c71 100644
--- a/tutorials/strands-agents-tutorial/05-hooks-state-and-reliability-controls.md
+++ b/tutorials/strands-agents-tutorial/05-hooks-state-and-reliability-controls.md
@@ -7,6 +7,9 @@ parent: Strands Agents Tutorial
 
 # Chapter 5: Hooks, State, and Reliability Controls
 
+Welcome to **Chapter 5: Hooks, State, and Reliability Controls**. In this part of **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter shows how to shape runtime behavior without breaking the simple programming model.
 
 ## Learning Goals
@@ -33,3 +36,598 @@ This chapter shows how to shape runtime behavior without breaking the simple pro
 You now have a safe pattern for applying runtime controls while preserving Strands' simplicity.
 
 Next: [Chapter 6: Multi-Agent and Advanced Patterns](06-multi-agent-and-advanced-patterns.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- tutorial slug: **strands-agents-tutorial**
+- chapter focus: **Chapter 5: Hooks, State, and Reliability Controls**
+- system context: **Strands Agents Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Hooks, State, and Reliability Controls`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Strands Python SDK Repository](https://github.com/strands-agents/sdk-python)
+- [Strands README](https://github.com/strands-agents/sdk-python/blob/main/README.md)
+- [Strands Documentation](https://strandsagents.com/latest/documentation/docs/)
+- [Strands Python Quickstart](https://strandsagents.com/latest/documentation/docs/user-guide/quickstart/python/)
+- [Strands MCP Client Architecture](https://github.com/strands-agents/sdk-python/blob/main/docs/MCP_CLIENT_ARCHITECTURE.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [CrewAI Tutorial](../crewai-tutorial/)
+- [Anything LLM Tutorial](../anything-llm-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Hooks, State, and Reliability Controls`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Hooks, State, and Reliability Controls
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Hooks, State, and Reliability Controls` as an operating subsystem inside **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Hooks, State, and Reliability Controls` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Strands Python SDK Repository](https://github.com/strands-agents/sdk-python)
+  Why it matters: authoritative reference on `Strands Python SDK Repository` (github.com).
+- [Strands README](https://github.com/strands-agents/sdk-python/blob/main/README.md)
+  Why it matters: authoritative reference on `Strands README` (github.com).
+- [Strands Documentation](https://strandsagents.com/latest/documentation/docs/)
+  Why it matters: authoritative reference on `Strands Documentation` (strandsagents.com).
+- [Strands Python Quickstart](https://strandsagents.com/latest/documentation/docs/user-guide/quickstart/python/)
+  Why it matters: authoritative reference on `Strands Python Quickstart` (strandsagents.com).
+- [Strands MCP Client Architecture](https://github.com/strands-agents/sdk-python/blob/main/docs/MCP_CLIENT_ARCHITECTURE.md)
+  Why it matters: authoritative reference on `Strands MCP Client Architecture` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Model Providers and Runtime Strategy](04-model-providers-and-runtime-strategy.md)
+- [Next Chapter: Chapter 6: Multi-Agent and Advanced Patterns](06-multi-agent-and-advanced-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/strands-agents-tutorial/06-multi-agent-and-advanced-patterns.md b/tutorials/strands-agents-tutorial/06-multi-agent-and-advanced-patterns.md
index f1288735..1ea32d41 100644
--- a/tutorials/strands-agents-tutorial/06-multi-agent-and-advanced-patterns.md
+++ b/tutorials/strands-agents-tutorial/06-multi-agent-and-advanced-patterns.md
@@ -7,6 +7,9 @@ parent: Strands Agents Tutorial
 
 # Chapter 6: Multi-Agent and Advanced Patterns
 
+Welcome to **Chapter 6: Multi-Agent and Advanced Patterns**. In this part of **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explores advanced usage beyond basic single-agent workflows.
 
 ## Learning Goals
@@ -33,3 +36,598 @@ This chapter explores advanced usage beyond basic single-agent workflows.
 You now have a roadmap for scaling Strands workflows without losing architectural control.
 
 Next: [Chapter 7: Deployment and Production Operations](07-deployment-and-production-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- tutorial slug: **strands-agents-tutorial**
+- chapter focus: **Chapter 6: Multi-Agent and Advanced Patterns**
+- system context: **Strands Agents Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Multi-Agent and Advanced Patterns`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Strands Python SDK Repository](https://github.com/strands-agents/sdk-python)
+- [Strands README](https://github.com/strands-agents/sdk-python/blob/main/README.md)
+- [Strands Documentation](https://strandsagents.com/latest/documentation/docs/)
+- [Strands Python Quickstart](https://strandsagents.com/latest/documentation/docs/user-guide/quickstart/python/)
+- [Strands MCP Client Architecture](https://github.com/strands-agents/sdk-python/blob/main/docs/MCP_CLIENT_ARCHITECTURE.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [CrewAI Tutorial](../crewai-tutorial/)
+- [Anything LLM Tutorial](../anything-llm-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Multi-Agent and Advanced Patterns`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Multi-Agent and Advanced Patterns
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Multi-Agent and Advanced Patterns` as an operating subsystem inside **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Multi-Agent and Advanced Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Strands Python SDK Repository](https://github.com/strands-agents/sdk-python)
+  Why it matters: authoritative reference on `Strands Python SDK Repository` (github.com).
+- [Strands README](https://github.com/strands-agents/sdk-python/blob/main/README.md)
+  Why it matters: authoritative reference on `Strands README` (github.com).
+- [Strands Documentation](https://strandsagents.com/latest/documentation/docs/)
+  Why it matters: authoritative reference on `Strands Documentation` (strandsagents.com).
+- [Strands Python Quickstart](https://strandsagents.com/latest/documentation/docs/user-guide/quickstart/python/)
+  Why it matters: authoritative reference on `Strands Python Quickstart` (strandsagents.com).
+- [Strands MCP Client Architecture](https://github.com/strands-agents/sdk-python/blob/main/docs/MCP_CLIENT_ARCHITECTURE.md)
+  Why it matters: authoritative reference on `Strands MCP Client Architecture` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Hooks, State, and Reliability Controls](05-hooks-state-and-reliability-controls.md)
+- [Next Chapter: Chapter 7: Deployment and Production Operations](07-deployment-and-production-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/strands-agents-tutorial/07-deployment-and-production-operations.md b/tutorials/strands-agents-tutorial/07-deployment-and-production-operations.md
index fedd08f4..5f0e53b4 100644
--- a/tutorials/strands-agents-tutorial/07-deployment-and-production-operations.md
+++ b/tutorials/strands-agents-tutorial/07-deployment-and-production-operations.md
@@ -7,6 +7,9 @@ parent: Strands Agents Tutorial
 
 # Chapter 7: Deployment and Production Operations
 
+Welcome to **Chapter 7: Deployment and Production Operations**. In this part of **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter outlines production rollout and operational governance for Strands agents.
 
 ## Learning Goals
@@ -34,3 +37,598 @@ This chapter outlines production rollout and operational governance for Strands
 You now have a deployment and operations baseline for production Strands usage.
 
 Next: [Chapter 8: Contribution Workflow and Ecosystem Extensions](08-contribution-workflow-and-ecosystem-extensions.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- tutorial slug: **strands-agents-tutorial**
+- chapter focus: **Chapter 7: Deployment and Production Operations**
+- system context: **Strands Agents Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Deployment and Production Operations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Strands Python SDK Repository](https://github.com/strands-agents/sdk-python)
+- [Strands README](https://github.com/strands-agents/sdk-python/blob/main/README.md)
+- [Strands Documentation](https://strandsagents.com/latest/documentation/docs/)
+- [Strands Python Quickstart](https://strandsagents.com/latest/documentation/docs/user-guide/quickstart/python/)
+- [Strands MCP Client Architecture](https://github.com/strands-agents/sdk-python/blob/main/docs/MCP_CLIENT_ARCHITECTURE.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [CrewAI Tutorial](../crewai-tutorial/)
+- [Anything LLM Tutorial](../anything-llm-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Deployment and Production Operations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Deployment and Production Operations
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Deployment and Production Operations` as an operating subsystem inside **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Deployment and Production Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Strands Python SDK Repository](https://github.com/strands-agents/sdk-python)
+  Why it matters: authoritative reference on `Strands Python SDK Repository` (github.com).
+- [Strands README](https://github.com/strands-agents/sdk-python/blob/main/README.md)
+  Why it matters: authoritative reference on `Strands README` (github.com).
+- [Strands Documentation](https://strandsagents.com/latest/documentation/docs/)
+  Why it matters: authoritative reference on `Strands Documentation` (strandsagents.com).
+- [Strands Python Quickstart](https://strandsagents.com/latest/documentation/docs/user-guide/quickstart/python/)
+  Why it matters: authoritative reference on `Strands Python Quickstart` (strandsagents.com).
+- [Strands MCP Client Architecture](https://github.com/strands-agents/sdk-python/blob/main/docs/MCP_CLIENT_ARCHITECTURE.md)
+  Why it matters: authoritative reference on `Strands MCP Client Architecture` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Multi-Agent and Advanced Patterns](06-multi-agent-and-advanced-patterns.md)
+- [Next Chapter: Chapter 8: Contribution Workflow and Ecosystem Extensions](08-contribution-workflow-and-ecosystem-extensions.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/strands-agents-tutorial/08-contribution-workflow-and-ecosystem-extensions.md b/tutorials/strands-agents-tutorial/08-contribution-workflow-and-ecosystem-extensions.md
index 6d7ad86b..dc763e9d 100644
--- a/tutorials/strands-agents-tutorial/08-contribution-workflow-and-ecosystem-extensions.md
+++ b/tutorials/strands-agents-tutorial/08-contribution-workflow-and-ecosystem-extensions.md
@@ -7,6 +7,9 @@ parent: Strands Agents Tutorial
 
 # Chapter 8: Contribution Workflow and Ecosystem Extensions
 
+Welcome to **Chapter 8: Contribution Workflow and Ecosystem Extensions**. In this part of **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers how to contribute effectively and extend the Strands ecosystem.
 
 ## Learning Goals
@@ -34,3 +37,597 @@ This chapter covers how to contribute effectively and extend the Strands ecosyst
 You now have a full Strands track from first agent to ecosystem-level contribution.
 
 Next tutorial: [ADK Python Tutorial](../adk-python-tutorial/)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- tutorial slug: **strands-agents-tutorial**
+- chapter focus: **Chapter 8: Contribution Workflow and Ecosystem Extensions**
+- system context: **Strands Agents Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Contribution Workflow and Ecosystem Extensions`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Strands Python SDK Repository](https://github.com/strands-agents/sdk-python)
+- [Strands README](https://github.com/strands-agents/sdk-python/blob/main/README.md)
+- [Strands Documentation](https://strandsagents.com/latest/documentation/docs/)
+- [Strands Python Quickstart](https://strandsagents.com/latest/documentation/docs/user-guide/quickstart/python/)
+- [Strands MCP Client Architecture](https://github.com/strands-agents/sdk-python/blob/main/docs/MCP_CLIENT_ARCHITECTURE.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [CrewAI Tutorial](../crewai-tutorial/)
+- [Anything LLM Tutorial](../anything-llm-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Contribution Workflow and Ecosystem Extensions`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Contribution Workflow and Ecosystem Extensions
+
+- tutorial context: **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Contribution Workflow and Ecosystem Extensions` as an operating subsystem inside **Strands Agents Tutorial: Model-Driven Agent Systems with Native MCP Support**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Contribution Workflow and Ecosystem Extensions` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Strands Python SDK Repository](https://github.com/strands-agents/sdk-python)
+  Why it matters: authoritative reference on `Strands Python SDK Repository` (github.com).
+- [Strands README](https://github.com/strands-agents/sdk-python/blob/main/README.md)
+  Why it matters: authoritative reference on `Strands README` (github.com).
+- [Strands Documentation](https://strandsagents.com/latest/documentation/docs/)
+  Why it matters: authoritative reference on `Strands Documentation` (strandsagents.com).
+- [Strands Python Quickstart](https://strandsagents.com/latest/documentation/docs/user-guide/quickstart/python/)
+  Why it matters: authoritative reference on `Strands Python Quickstart` (strandsagents.com).
+- [Strands MCP Client Architecture](https://github.com/strands-agents/sdk-python/blob/main/docs/MCP_CLIENT_ARCHITECTURE.md)
+  Why it matters: authoritative reference on `Strands MCP Client Architecture` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Deployment and Production Operations](07-deployment-and-production-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/supabase-tutorial/01-getting-started.md b/tutorials/supabase-tutorial/01-getting-started.md
index 71af02eb..3b595717 100644
--- a/tutorials/supabase-tutorial/01-getting-started.md
+++ b/tutorials/supabase-tutorial/01-getting-started.md
@@ -638,3 +638,48 @@ Now that you have a working Supabase application, let's dive deeper into databas
 4. Create a mobile version using React Native
 
 *What kind of application are you most excited to build with Supabase's powerful features?* 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `error`, `tasks`, `supabase` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Supabase` as an operating subsystem inside **Supabase Tutorial: Building Modern Backend Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `task`, `auth`, `console` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Supabase` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `error`.
+2. **Input normalization**: shape incoming data so `tasks` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `supabase`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/supabase/supabase)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `error` and `tasks` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Database Design & Management](02-database-design.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/supabase-tutorial/02-database-design.md b/tutorials/supabase-tutorial/02-database-design.md
index 934c5aba..8f9eea1f 100644
--- a/tutorials/supabase-tutorial/02-database-design.md
+++ b/tutorials/supabase-tutorial/02-database-design.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Database Design & Management
 
+Welcome to **Chapter 2: Database Design & Management**. In this part of **Supabase Tutorial: Building Modern Backend Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 1](01-getting-started.md), you set up a Supabase project, created your first table, and built a working task application. Now it is time to go deeper into the heart of Supabase -- PostgreSQL. A well-designed database schema is the foundation of every reliable application. In this chapter you will learn how to model data with tables, relationships, and constraints; manage schema changes through migrations; seed development data; index for performance; and lay the groundwork for Row Level Security.
 
 ## Why Database Design Matters
@@ -710,3 +713,49 @@ Your database is designed, indexed, and protected with RLS. In [Chapter 3: Authe
 ---
 
 *Built with insights from the [Supabase](https://github.com/supabase/supabase) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `CREATE`, `status`, `supabase` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Database Design & Management` as an operating subsystem inside **Supabase Tutorial: Building Modern Backend Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `tasks`, `created_at`, `TABLE` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Database Design & Management` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `CREATE`.
+2. **Input normalization**: shape incoming data so `status` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `supabase`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/supabase/supabase)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `CREATE` and `status` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Supabase](01-getting-started.md)
+- [Next Chapter: Chapter 3: Authentication & Authorization](03-authentication.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/supabase-tutorial/03-authentication.md b/tutorials/supabase-tutorial/03-authentication.md
index bbe0f4f4..c86910c5 100644
--- a/tutorials/supabase-tutorial/03-authentication.md
+++ b/tutorials/supabase-tutorial/03-authentication.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Authentication & Authorization
 
+Welcome to **Chapter 3: Authentication & Authorization**. In this part of **Supabase Tutorial: Building Modern Backend Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 2](02-database-design.md), you designed a robust database schema with tables, relationships, and Row Level Security foundations. Now you need to protect that data with a proper authentication system. Supabase Auth provides a complete identity layer built on top of GoTrue -- supporting email/password login, magic links, phone OTP, and over a dozen OAuth providers. In this chapter you will implement multiple auth strategies, manage user profiles with database triggers, build a session-aware React application, and write production-grade RLS policies.
 
 ## How Supabase Auth Works
@@ -797,3 +800,49 @@ Your users can sign up, log in, and access only the data they are authorized to
 ---
 
 *Built with insights from the [Supabase](https://github.com/supabase/supabase) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `error`, `auth`, `supabase` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Authentication & Authorization` as an operating subsystem inside **Supabase Tutorial: Building Modern Backend Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `user`, `email`, `throw` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Authentication & Authorization` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `error`.
+2. **Input normalization**: shape incoming data so `auth` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `supabase`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/supabase/supabase)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `error` and `auth` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Database Design & Management](02-database-design.md)
+- [Next Chapter: Chapter 4: Real-time Features](04-realtime-features.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/supabase-tutorial/04-realtime-features.md b/tutorials/supabase-tutorial/04-realtime-features.md
index fccd619c..09dbb966 100644
--- a/tutorials/supabase-tutorial/04-realtime-features.md
+++ b/tutorials/supabase-tutorial/04-realtime-features.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Real-time Features
 
+Welcome to **Chapter 4: Real-time Features**. In this part of **Supabase Tutorial: Building Modern Backend Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 3](03-authentication.md), you built a complete authentication system with multiple sign-in methods and team-based RLS policies. Now it is time to make your application come alive. Supabase Realtime lets you subscribe to database changes, broadcast messages between clients, and track user presence -- all over WebSocket connections. In this chapter you will subscribe to table changes with `postgres_changes`, build a live chat application, implement collaborative presence indicators, and handle reconnection gracefully.
 
 ## How Supabase Realtime Works
@@ -971,3 +974,49 @@ Your application now has live data synchronization. In [Chapter 5: Storage & Fil
 ---
 
 *Built with insights from the [Supabase](https://github.com/supabase/supabase) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `supabase`, `payload`, `channel` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Real-time Features` as an operating subsystem inside **Supabase Tutorial: Building Modern Backend Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `messages`, `status`, `prev` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Real-time Features` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `supabase`.
+2. **Input normalization**: shape incoming data so `payload` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `channel`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/supabase/supabase)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `supabase` and `payload` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Authentication & Authorization](03-authentication.md)
+- [Next Chapter: Chapter 5: Storage & File Management](05-storage-management.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/supabase-tutorial/05-storage-management.md b/tutorials/supabase-tutorial/05-storage-management.md
index d054662d..56f686ea 100644
--- a/tutorials/supabase-tutorial/05-storage-management.md
+++ b/tutorials/supabase-tutorial/05-storage-management.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Storage & File Management
 
+Welcome to **Chapter 5: Storage & File Management**. In this part of **Supabase Tutorial: Building Modern Backend Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 4](04-realtime-features.md), you added live data synchronization with real-time subscriptions, broadcast, and presence. Now your application needs to handle files -- profile avatars, document uploads, image galleries, and media attachments. Supabase Storage is an S3-compatible object storage service that integrates directly with your database and RLS policies. In this chapter you will create and configure storage buckets, upload and download files securely, generate signed and public URLs, implement image transformations, build a complete file management system, and protect files with granular access policies.
 
 ## How Supabase Storage Works
@@ -827,3 +830,49 @@ Your application can now store and serve files securely. In [Chapter 6: Edge Fun
 ---
 
 *Built with insights from the [Supabase](https://github.com/supabase/supabase) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `error`, `bucket`, `storage` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Storage & File Management` as an operating subsystem inside **Supabase Tutorial: Building Modern Backend Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `supabase`, `file`, `path` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Storage & File Management` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `error`.
+2. **Input normalization**: shape incoming data so `bucket` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `storage`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/supabase/supabase)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `error` and `bucket` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Real-time Features](04-realtime-features.md)
+- [Next Chapter: Chapter 6: Edge Functions](06-edge-functions.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/supabase-tutorial/06-edge-functions.md b/tutorials/supabase-tutorial/06-edge-functions.md
index 7cc46bd4..73ae95b1 100644
--- a/tutorials/supabase-tutorial/06-edge-functions.md
+++ b/tutorials/supabase-tutorial/06-edge-functions.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Edge Functions
 
+Welcome to **Chapter 6: Edge Functions**. In this part of **Supabase Tutorial: Building Modern Backend Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 5](05-storage-management.md), you built a complete file management system with uploads, transformations, and access control. But some backend logic cannot live in the database or the client -- payment processing, third-party API calls, webhook handling, email sending, and data transformations all need a server-side runtime. Supabase Edge Functions are serverless TypeScript functions that run on Deno Deploy, executing close to your users at the edge. In this chapter you will create, test, and deploy Edge Functions; secure them with JWT verification; handle webhooks from services like Stripe; build custom API endpoints; connect functions to your database and storage; and set up scheduled tasks.
 
 ## How Edge Functions Work
@@ -691,3 +694,49 @@ Your application now has custom backend logic at the edge. In [Chapter 7: Advanc
 ---
 
 *Built with insights from the [Supabase](https://github.com/supabase/supabase) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `supabase`, `error`, `functions` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Edge Functions` as an operating subsystem inside **Supabase Tutorial: Building Modern Backend Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `headers`, `json`, `user` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Edge Functions` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `supabase`.
+2. **Input normalization**: shape incoming data so `error` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `functions`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/supabase/supabase)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `supabase` and `error` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Storage & File Management](05-storage-management.md)
+- [Next Chapter: Chapter 7: Advanced Queries & RLS](07-advanced-queries.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/supabase-tutorial/07-advanced-queries.md b/tutorials/supabase-tutorial/07-advanced-queries.md
index dc857d2a..3700b224 100644
--- a/tutorials/supabase-tutorial/07-advanced-queries.md
+++ b/tutorials/supabase-tutorial/07-advanced-queries.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Advanced Queries & RLS
 
+Welcome to **Chapter 7: Advanced Queries & RLS**. In this part of **Supabase Tutorial: Building Modern Backend Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 6](06-edge-functions.md), you built serverless backend logic with Edge Functions for payments, emails, and webhooks. Now it is time to push your database skills further. This chapter covers the advanced query patterns and security hardening techniques that separate a prototype from a production application. You will implement cursor-based pagination, full-text search with ranking, complex aggregations with database functions, multi-tenant RLS policies, query performance analysis with `EXPLAIN ANALYZE`, and materialized views for expensive computations.
 
 ## Query Architecture Overview
@@ -717,3 +720,49 @@ Your queries are fast, your search is relevant, and your RLS policies are harden
 ---
 
 *Built with insights from the [Supabase](https://github.com/supabase/supabase) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `tasks`, `SELECT`, `team_id` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Advanced Queries & RLS` as an operating subsystem inside **Supabase Tutorial: Building Modern Backend Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `projects`, `CREATE`, `WHERE` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Advanced Queries & RLS` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `tasks`.
+2. **Input normalization**: shape incoming data so `SELECT` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `team_id`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/supabase/supabase)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `tasks` and `SELECT` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Edge Functions](06-edge-functions.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/supabase-tutorial/08-production-deployment.md b/tutorials/supabase-tutorial/08-production-deployment.md
index 349b37ed..cb962f75 100644
--- a/tutorials/supabase-tutorial/08-production-deployment.md
+++ b/tutorials/supabase-tutorial/08-production-deployment.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **Supabase Tutorial: Building Modern Backend Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 7](07-advanced-queries.md), you hardened your queries and RLS policies for performance and security. Now it is time to ship your application to real users. Moving from development to production involves more than flipping a switch -- you need proper environment management, monitoring, backups, security hardening, performance tuning, and a systematic launch process. This chapter walks through every step of preparing and operating a production Supabase application.
 
 ## Production Architecture Overview
@@ -872,3 +875,48 @@ Congratulations -- you have built, secured, and deployed a production-ready Supa
 ---
 
 *Built with insights from the [Supabase](https://github.com/supabase/supabase) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `supabase`, `schemaname`, `name` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **Supabase Tutorial: Building Modern Backend Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `secrets`, `tablename`, `checks` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `supabase`.
+2. **Input normalization**: shape incoming data so `schemaname` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `name`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/supabase/supabase)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `supabase` and `schemaname` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Advanced Queries & RLS](07-advanced-queries.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/superagi-tutorial/01-getting-started.md b/tutorials/superagi-tutorial/01-getting-started.md
index db34562a..95ea08f0 100644
--- a/tutorials/superagi-tutorial/01-getting-started.md
+++ b/tutorials/superagi-tutorial/01-getting-started.md
@@ -562,3 +562,50 @@ Now that you have a working SuperAGI setup, let's explore the agent architecture
 4. Monitor agent performance and health metrics
 
 *What kind of autonomous agent would you build first with SuperAGI?* 🤖
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `agent`, `print`, `Agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with SuperAGI` as an operating subsystem inside **SuperAGI Tutorial: Production-Ready Autonomous AI Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `memory`, `name`, `superagi` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with SuperAGI` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `agent`.
+2. **Input normalization**: shape incoming data so `print` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/TransformerOptimus/SuperAGI)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `agent` and `print` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Agent Architecture](02-agent-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/superagi-tutorial/02-agent-architecture.md b/tutorials/superagi-tutorial/02-agent-architecture.md
index 7a43881f..b1a5fcc0 100644
--- a/tutorials/superagi-tutorial/02-agent-architecture.md
+++ b/tutorials/superagi-tutorial/02-agent-architecture.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Agent Architecture
 
+Welcome to **Chapter 2: Agent Architecture**. In this part of **SuperAGI Tutorial: Production-Ready Autonomous AI Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Understand SuperAGI's agent design patterns, reasoning engines, and the components that enable autonomous behavior.
 
 ## Overview
@@ -525,3 +528,51 @@ Now that you understand agent architecture, let's explore Tool Integration in Ch
 **Ready for Chapter 3?** [Tool Integration](03-tool-integration.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `dict`, `reasoning` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Agent Architecture` as an operating subsystem inside **SuperAGI Tutorial: Production-Ready Autonomous AI Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `context`, `config`, `agent` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Agent Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `dict` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `reasoning`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/TransformerOptimus/SuperAGI)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `dict` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with SuperAGI](01-getting-started.md)
+- [Next Chapter: Chapter 3: Tool Integration](03-tool-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/superagi-tutorial/03-tool-integration.md b/tutorials/superagi-tutorial/03-tool-integration.md
index f4b61803..2b5bc83b 100644
--- a/tutorials/superagi-tutorial/03-tool-integration.md
+++ b/tutorials/superagi-tutorial/03-tool-integration.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Tool Integration
 
+Welcome to **Chapter 3: Tool Integration**. In this part of **SuperAGI Tutorial: Production-Ready Autonomous AI Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Connect SuperAGI agents to external tools, APIs, and services for enhanced capabilities.
 
 ## Overview
@@ -668,3 +671,51 @@ Now that you can integrate tools, let's explore Memory & Learning in Chapter 4 f
 **Ready for Chapter 4?** [Memory & Learning](04-memory-learning.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `tool`, `results` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Tool Integration` as an operating subsystem inside **SuperAGI Tutorial: Production-Ready Autonomous AI Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `result`, `execute`, `error` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Tool Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `tool` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `results`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/TransformerOptimus/SuperAGI)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `tool` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Agent Architecture](02-agent-architecture.md)
+- [Next Chapter: Chapter 4: Memory & Learning](04-memory-learning.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/superagi-tutorial/04-memory-learning.md b/tutorials/superagi-tutorial/04-memory-learning.md
index a5231711..7e972911 100644
--- a/tutorials/superagi-tutorial/04-memory-learning.md
+++ b/tutorials/superagi-tutorial/04-memory-learning.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Memory & Learning
 
+Welcome to **Chapter 4: Memory & Learning**. In this part of **SuperAGI Tutorial: Production-Ready Autonomous AI Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Implement persistent memory systems and learning mechanisms for agents that improve over time.
 
 ## Overview
@@ -672,3 +675,51 @@ Now that you understand memory and learning, let's explore Task Planning in Chap
 **Ready for Chapter 5?** [Task Planning](05-task-planning.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `memory`, `episode` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Memory & Learning` as an operating subsystem inside **SuperAGI Tutorial: Production-Ready Autonomous AI Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `dict`, `append`, `content` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Memory & Learning` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `memory` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `episode`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/TransformerOptimus/SuperAGI)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `memory` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Tool Integration](03-tool-integration.md)
+- [Next Chapter: Chapter 5: Task Planning](05-task-planning.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/superagi-tutorial/05-task-planning.md b/tutorials/superagi-tutorial/05-task-planning.md
index 736d1378..bf43c0cd 100644
--- a/tutorials/superagi-tutorial/05-task-planning.md
+++ b/tutorials/superagi-tutorial/05-task-planning.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Task Planning
 
+Welcome to **Chapter 5: Task Planning**. In this part of **SuperAGI Tutorial: Production-Ready Autonomous AI Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master advanced planning techniques for goal decomposition, execution strategies, and adaptive replanning.
 
 ## Overview
@@ -685,3 +688,51 @@ Now that you can plan complex tasks, let's explore Multi-Agent Systems in Chapte
 **Ready for Chapter 6?** [Multi-Agent Systems](06-multi-agent-systems.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `task`, `task_id` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Task Planning` as an operating subsystem inside **SuperAGI Tutorial: Production-Ready Autonomous AI Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `tasks`, `dict`, `context` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Task Planning` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `task` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `task_id`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/TransformerOptimus/SuperAGI)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `task` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Memory & Learning](04-memory-learning.md)
+- [Next Chapter: Chapter 6: Multi-Agent Systems](06-multi-agent-systems.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/superagi-tutorial/06-multi-agent-systems.md b/tutorials/superagi-tutorial/06-multi-agent-systems.md
index 9a8ca4a6..c883eb38 100644
--- a/tutorials/superagi-tutorial/06-multi-agent-systems.md
+++ b/tutorials/superagi-tutorial/06-multi-agent-systems.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Multi-Agent Systems
 
+Welcome to **Chapter 6: Multi-Agent Systems**. In this part of **SuperAGI Tutorial: Production-Ready Autonomous AI Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Coordinate multiple agents for complex tasks through communication, collaboration, and orchestration patterns.
 
 ## Overview
@@ -735,3 +738,51 @@ Now that you can coordinate agents, let's explore Deployment & Scaling in Chapte
 **Ready for Chapter 7?** [Deployment & Scaling](07-deployment-scaling.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `task`, `message` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Multi-Agent Systems` as an operating subsystem inside **SuperAGI Tutorial: Production-Ready Autonomous AI Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `content`, `agent_id`, `message_bus` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Multi-Agent Systems` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `task` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `message`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/TransformerOptimus/SuperAGI)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `task` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Task Planning](05-task-planning.md)
+- [Next Chapter: Chapter 7: Deployment & Scaling](07-deployment-scaling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/superagi-tutorial/07-deployment-scaling.md b/tutorials/superagi-tutorial/07-deployment-scaling.md
index b4f0fee2..41e83098 100644
--- a/tutorials/superagi-tutorial/07-deployment-scaling.md
+++ b/tutorials/superagi-tutorial/07-deployment-scaling.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Deployment & Scaling
 
+Welcome to **Chapter 7: Deployment & Scaling**. In this part of **SuperAGI Tutorial: Production-Ready Autonomous AI Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy SuperAGI to production with containerization, orchestration, monitoring, and horizontal scaling strategies.
 
 ## Overview
@@ -1012,3 +1015,51 @@ Now that you can deploy and scale SuperAGI, let's explore Advanced Features in C
 **Ready for Chapter 8?** [Advanced Features](08-advanced-features.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `superagi`, `task` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Deployment & Scaling` as an operating subsystem inside **SuperAGI Tutorial: Production-Ready Autonomous AI Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `agent`, `name`, `Dict` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Deployment & Scaling` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `superagi` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `task`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/TransformerOptimus/SuperAGI)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `superagi` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Multi-Agent Systems](06-multi-agent-systems.md)
+- [Next Chapter: Chapter 8: Advanced Features](08-advanced-features.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/superagi-tutorial/08-advanced-features.md b/tutorials/superagi-tutorial/08-advanced-features.md
index 4c50e42b..b344548d 100644
--- a/tutorials/superagi-tutorial/08-advanced-features.md
+++ b/tutorials/superagi-tutorial/08-advanced-features.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Advanced Features
 
+Welcome to **Chapter 8: Advanced Features**. In this part of **SuperAGI Tutorial: Production-Ready Autonomous AI Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master custom agent development, plugin architecture, enterprise integrations, and advanced SuperAGI capabilities.
 
 ## Overview
@@ -1092,3 +1095,50 @@ Congratulations! You've completed the SuperAGI tutorial. You now have the knowle
 ---
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `agent`, `Dict` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Advanced Features` as an operating subsystem inside **SuperAGI Tutorial: Production-Ready Autonomous AI Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `task`, `problem`, `result` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Advanced Features` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `agent` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Dict`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/TransformerOptimus/SuperAGI)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `agent` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Deployment & Scaling](07-deployment-scaling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/superset-terminal-tutorial/01-getting-started.md b/tutorials/superset-terminal-tutorial/01-getting-started.md
index 93fc507d..70b3d78c 100644
--- a/tutorials/superset-terminal-tutorial/01-getting-started.md
+++ b/tutorials/superset-terminal-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Superset Terminal Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Superset running for first-time multi-agent workspace management.
 
 ## Quick Start
@@ -30,3 +33,613 @@ bun run dev
 You now have a running Superset baseline for workspace-based agent orchestration.
 
 Next: [Chapter 2: Worktree Isolation and Workspace Model](02-worktree-isolation-and-workspace-model.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- tutorial slug: **superset-terminal-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Superset Terminal Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Superset Repository](https://github.com/superset-sh/superset)
+- [Superset README](https://github.com/superset-sh/superset/blob/main/README.md)
+- [Workspace orchestrator](https://github.com/superset-sh/superset/blob/main/apps/cli/src/lib/orchestrators/workspace-orchestrator.ts)
+- [Workspace init manager](https://github.com/superset-sh/superset/blob/main/apps/desktop/src/main/lib/workspace-init-manager.ts)
+- [Shared agent package](https://github.com/superset-sh/superset/blob/main/packages/agent/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Squad Tutorial](../claude-squad-tutorial/)
+- [Kilo Code Tutorial](../kilocode-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 1: Getting Started
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `superset`, `clone`, `https` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `github`, `install` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `superset`.
+2. **Input normalization**: shape incoming data so `clone` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `https`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Superset Repository](https://github.com/superset-sh/superset)
+  Why it matters: authoritative reference on `Superset Repository` (github.com).
+- [Superset README](https://github.com/superset-sh/superset/blob/main/README.md)
+  Why it matters: authoritative reference on `Superset README` (github.com).
+- [Workspace orchestrator](https://github.com/superset-sh/superset/blob/main/apps/cli/src/lib/orchestrators/workspace-orchestrator.ts)
+  Why it matters: authoritative reference on `Workspace orchestrator` (github.com).
+- [Workspace init manager](https://github.com/superset-sh/superset/blob/main/apps/desktop/src/main/lib/workspace-init-manager.ts)
+  Why it matters: authoritative reference on `Workspace init manager` (github.com).
+- [Shared agent package](https://github.com/superset-sh/superset/blob/main/packages/agent/README.md)
+  Why it matters: authoritative reference on `Shared agent package` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `superset` and `clone` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Worktree Isolation and Workspace Model](02-worktree-isolation-and-workspace-model.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/superset-terminal-tutorial/02-worktree-isolation-and-workspace-model.md b/tutorials/superset-terminal-tutorial/02-worktree-isolation-and-workspace-model.md
index 42b59e96..4d3df394 100644
--- a/tutorials/superset-terminal-tutorial/02-worktree-isolation-and-workspace-model.md
+++ b/tutorials/superset-terminal-tutorial/02-worktree-isolation-and-workspace-model.md
@@ -7,6 +7,9 @@ parent: Superset Terminal Tutorial
 
 # Chapter 2: Worktree Isolation and Workspace Model
 
+Welcome to **Chapter 2: Worktree Isolation and Workspace Model**. In this part of **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Superset isolates each active task in its own git worktree and workspace context.
 
 ## Isolation Benefits
@@ -25,3 +28,610 @@ Superset isolates each active task in its own git worktree and workspace context
 You now understand how Superset prevents multi-agent interference through workspace isolation.
 
 Next: [Chapter 3: Workspace Orchestration Lifecycle](03-workspace-orchestration-lifecycle.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- tutorial slug: **superset-terminal-tutorial**
+- chapter focus: **Chapter 2: Worktree Isolation and Workspace Model**
+- system context: **Superset Terminal Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Worktree Isolation and Workspace Model`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Superset Repository](https://github.com/superset-sh/superset)
+- [Superset README](https://github.com/superset-sh/superset/blob/main/README.md)
+- [Workspace orchestrator](https://github.com/superset-sh/superset/blob/main/apps/cli/src/lib/orchestrators/workspace-orchestrator.ts)
+- [Workspace init manager](https://github.com/superset-sh/superset/blob/main/apps/desktop/src/main/lib/workspace-init-manager.ts)
+- [Shared agent package](https://github.com/superset-sh/superset/blob/main/packages/agent/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Squad Tutorial](../claude-squad-tutorial/)
+- [Kilo Code Tutorial](../kilocode-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Worktree Isolation and Workspace Model`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 2: Worktree Isolation and Workspace Model
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Worktree Isolation and Workspace Model` as an operating subsystem inside **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Worktree Isolation and Workspace Model` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Superset Repository](https://github.com/superset-sh/superset)
+  Why it matters: authoritative reference on `Superset Repository` (github.com).
+- [Superset README](https://github.com/superset-sh/superset/blob/main/README.md)
+  Why it matters: authoritative reference on `Superset README` (github.com).
+- [Workspace orchestrator](https://github.com/superset-sh/superset/blob/main/apps/cli/src/lib/orchestrators/workspace-orchestrator.ts)
+  Why it matters: authoritative reference on `Workspace orchestrator` (github.com).
+- [Workspace init manager](https://github.com/superset-sh/superset/blob/main/apps/desktop/src/main/lib/workspace-init-manager.ts)
+  Why it matters: authoritative reference on `Workspace init manager` (github.com).
+- [Shared agent package](https://github.com/superset-sh/superset/blob/main/packages/agent/README.md)
+  Why it matters: authoritative reference on `Shared agent package` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Workspace Orchestration Lifecycle](03-workspace-orchestration-lifecycle.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/superset-terminal-tutorial/03-workspace-orchestration-lifecycle.md b/tutorials/superset-terminal-tutorial/03-workspace-orchestration-lifecycle.md
index 414ff8a2..537ef07e 100644
--- a/tutorials/superset-terminal-tutorial/03-workspace-orchestration-lifecycle.md
+++ b/tutorials/superset-terminal-tutorial/03-workspace-orchestration-lifecycle.md
@@ -7,6 +7,9 @@ parent: Superset Terminal Tutorial
 
 # Chapter 3: Workspace Orchestration Lifecycle
 
+Welcome to **Chapter 3: Workspace Orchestration Lifecycle**. In this part of **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Superset orchestrates workspace CRUD and related cascade operations for processes and diffs.
 
 ## Lifecycle Responsibilities
@@ -27,3 +30,610 @@ Superset orchestrates workspace CRUD and related cascade operations for processe
 You now have a concrete lifecycle model for Superset workspace management.
 
 Next: [Chapter 4: Multi-Agent Program Compatibility](04-multi-agent-program-compatibility.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- tutorial slug: **superset-terminal-tutorial**
+- chapter focus: **Chapter 3: Workspace Orchestration Lifecycle**
+- system context: **Superset Terminal Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Workspace Orchestration Lifecycle`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Superset Repository](https://github.com/superset-sh/superset)
+- [Superset README](https://github.com/superset-sh/superset/blob/main/README.md)
+- [Workspace orchestrator](https://github.com/superset-sh/superset/blob/main/apps/cli/src/lib/orchestrators/workspace-orchestrator.ts)
+- [Workspace init manager](https://github.com/superset-sh/superset/blob/main/apps/desktop/src/main/lib/workspace-init-manager.ts)
+- [Shared agent package](https://github.com/superset-sh/superset/blob/main/packages/agent/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Squad Tutorial](../claude-squad-tutorial/)
+- [Kilo Code Tutorial](../kilocode-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Workspace Orchestration Lifecycle`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 3: Workspace Orchestration Lifecycle
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Workspace Orchestration Lifecycle` as an operating subsystem inside **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Workspace Orchestration Lifecycle` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Superset Repository](https://github.com/superset-sh/superset)
+  Why it matters: authoritative reference on `Superset Repository` (github.com).
+- [Superset README](https://github.com/superset-sh/superset/blob/main/README.md)
+  Why it matters: authoritative reference on `Superset README` (github.com).
+- [Workspace orchestrator](https://github.com/superset-sh/superset/blob/main/apps/cli/src/lib/orchestrators/workspace-orchestrator.ts)
+  Why it matters: authoritative reference on `Workspace orchestrator` (github.com).
+- [Workspace init manager](https://github.com/superset-sh/superset/blob/main/apps/desktop/src/main/lib/workspace-init-manager.ts)
+  Why it matters: authoritative reference on `Workspace init manager` (github.com).
+- [Shared agent package](https://github.com/superset-sh/superset/blob/main/packages/agent/README.md)
+  Why it matters: authoritative reference on `Shared agent package` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Worktree Isolation and Workspace Model](02-worktree-isolation-and-workspace-model.md)
+- [Next Chapter: Chapter 4: Multi-Agent Program Compatibility](04-multi-agent-program-compatibility.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/superset-terminal-tutorial/04-multi-agent-program-compatibility.md b/tutorials/superset-terminal-tutorial/04-multi-agent-program-compatibility.md
index 6a44fa7a..827702c7 100644
--- a/tutorials/superset-terminal-tutorial/04-multi-agent-program-compatibility.md
+++ b/tutorials/superset-terminal-tutorial/04-multi-agent-program-compatibility.md
@@ -7,6 +7,9 @@ parent: Superset Terminal Tutorial
 
 # Chapter 4: Multi-Agent Program Compatibility
 
+Welcome to **Chapter 4: Multi-Agent Program Compatibility**. In this part of **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Superset is designed to run any terminal-native coding agent, not only one vendor stack.
 
 ## Supported Patterns
@@ -25,3 +28,610 @@ Superset is designed to run any terminal-native coding agent, not only one vendo
 You now know how Superset functions as a universal orchestrator for heterogeneous agent stacks.
 
 Next: [Chapter 5: Monitoring, Diff, and Review Workflow](05-monitoring-diff-and-review-workflow.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- tutorial slug: **superset-terminal-tutorial**
+- chapter focus: **Chapter 4: Multi-Agent Program Compatibility**
+- system context: **Superset Terminal Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Multi-Agent Program Compatibility`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Superset Repository](https://github.com/superset-sh/superset)
+- [Superset README](https://github.com/superset-sh/superset/blob/main/README.md)
+- [Workspace orchestrator](https://github.com/superset-sh/superset/blob/main/apps/cli/src/lib/orchestrators/workspace-orchestrator.ts)
+- [Workspace init manager](https://github.com/superset-sh/superset/blob/main/apps/desktop/src/main/lib/workspace-init-manager.ts)
+- [Shared agent package](https://github.com/superset-sh/superset/blob/main/packages/agent/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Squad Tutorial](../claude-squad-tutorial/)
+- [Kilo Code Tutorial](../kilocode-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Multi-Agent Program Compatibility`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 4: Multi-Agent Program Compatibility
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Multi-Agent Program Compatibility` as an operating subsystem inside **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Multi-Agent Program Compatibility` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Superset Repository](https://github.com/superset-sh/superset)
+  Why it matters: authoritative reference on `Superset Repository` (github.com).
+- [Superset README](https://github.com/superset-sh/superset/blob/main/README.md)
+  Why it matters: authoritative reference on `Superset README` (github.com).
+- [Workspace orchestrator](https://github.com/superset-sh/superset/blob/main/apps/cli/src/lib/orchestrators/workspace-orchestrator.ts)
+  Why it matters: authoritative reference on `Workspace orchestrator` (github.com).
+- [Workspace init manager](https://github.com/superset-sh/superset/blob/main/apps/desktop/src/main/lib/workspace-init-manager.ts)
+  Why it matters: authoritative reference on `Workspace init manager` (github.com).
+- [Shared agent package](https://github.com/superset-sh/superset/blob/main/packages/agent/README.md)
+  Why it matters: authoritative reference on `Shared agent package` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Workspace Orchestration Lifecycle](03-workspace-orchestration-lifecycle.md)
+- [Next Chapter: Chapter 5: Monitoring, Diff, and Review Workflow](05-monitoring-diff-and-review-workflow.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/superset-terminal-tutorial/05-monitoring-diff-and-review-workflow.md b/tutorials/superset-terminal-tutorial/05-monitoring-diff-and-review-workflow.md
index 7f3c8745..84a3fb8d 100644
--- a/tutorials/superset-terminal-tutorial/05-monitoring-diff-and-review-workflow.md
+++ b/tutorials/superset-terminal-tutorial/05-monitoring-diff-and-review-workflow.md
@@ -7,6 +7,9 @@ parent: Superset Terminal Tutorial
 
 # Chapter 5: Monitoring, Diff, and Review Workflow
 
+Welcome to **Chapter 5: Monitoring, Diff, and Review Workflow**. In this part of **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Superset centralizes status monitoring and diff review so human decisions can happen faster across many agent tasks.
 
 ## Review Loop
@@ -24,3 +27,610 @@ Superset centralizes status monitoring and diff review so human decisions can ha
 You now have a review-first flow for safely scaling agent throughput.
 
 Next: [Chapter 6: Setup/Teardown Presets and Automation](06-setup-teardown-presets-and-automation.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- tutorial slug: **superset-terminal-tutorial**
+- chapter focus: **Chapter 5: Monitoring, Diff, and Review Workflow**
+- system context: **Superset Terminal Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Monitoring, Diff, and Review Workflow`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Superset Repository](https://github.com/superset-sh/superset)
+- [Superset README](https://github.com/superset-sh/superset/blob/main/README.md)
+- [Workspace orchestrator](https://github.com/superset-sh/superset/blob/main/apps/cli/src/lib/orchestrators/workspace-orchestrator.ts)
+- [Workspace init manager](https://github.com/superset-sh/superset/blob/main/apps/desktop/src/main/lib/workspace-init-manager.ts)
+- [Shared agent package](https://github.com/superset-sh/superset/blob/main/packages/agent/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Squad Tutorial](../claude-squad-tutorial/)
+- [Kilo Code Tutorial](../kilocode-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Monitoring, Diff, and Review Workflow`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 5: Monitoring, Diff, and Review Workflow
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Monitoring, Diff, and Review Workflow` as an operating subsystem inside **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Monitoring, Diff, and Review Workflow` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Superset Repository](https://github.com/superset-sh/superset)
+  Why it matters: authoritative reference on `Superset Repository` (github.com).
+- [Superset README](https://github.com/superset-sh/superset/blob/main/README.md)
+  Why it matters: authoritative reference on `Superset README` (github.com).
+- [Workspace orchestrator](https://github.com/superset-sh/superset/blob/main/apps/cli/src/lib/orchestrators/workspace-orchestrator.ts)
+  Why it matters: authoritative reference on `Workspace orchestrator` (github.com).
+- [Workspace init manager](https://github.com/superset-sh/superset/blob/main/apps/desktop/src/main/lib/workspace-init-manager.ts)
+  Why it matters: authoritative reference on `Workspace init manager` (github.com).
+- [Shared agent package](https://github.com/superset-sh/superset/blob/main/packages/agent/README.md)
+  Why it matters: authoritative reference on `Shared agent package` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Multi-Agent Program Compatibility](04-multi-agent-program-compatibility.md)
+- [Next Chapter: Chapter 6: Setup/Teardown Presets and Automation](06-setup-teardown-presets-and-automation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/superset-terminal-tutorial/06-setup-teardown-presets-and-automation.md b/tutorials/superset-terminal-tutorial/06-setup-teardown-presets-and-automation.md
index 09fac4e1..3acfff46 100644
--- a/tutorials/superset-terminal-tutorial/06-setup-teardown-presets-and-automation.md
+++ b/tutorials/superset-terminal-tutorial/06-setup-teardown-presets-and-automation.md
@@ -7,6 +7,9 @@ parent: Superset Terminal Tutorial
 
 # Chapter 6: Setup/Teardown Presets and Automation
 
+Welcome to **Chapter 6: Setup/Teardown Presets and Automation**. In this part of **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Superset supports workspace automation through setup/teardown script presets.
 
 ## Preset Configuration
@@ -30,3 +33,614 @@ Example `.superset/config.json`:
 You now understand how to standardize workspace initialization and cleanup workflows.
 
 Next: [Chapter 7: Runtime and Package Architecture](07-runtime-and-package-architecture.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- tutorial slug: **superset-terminal-tutorial**
+- chapter focus: **Chapter 6: Setup/Teardown Presets and Automation**
+- system context: **Superset Terminal Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Setup/Teardown Presets and Automation`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Superset Repository](https://github.com/superset-sh/superset)
+- [Superset README](https://github.com/superset-sh/superset/blob/main/README.md)
+- [Workspace orchestrator](https://github.com/superset-sh/superset/blob/main/apps/cli/src/lib/orchestrators/workspace-orchestrator.ts)
+- [Workspace init manager](https://github.com/superset-sh/superset/blob/main/apps/desktop/src/main/lib/workspace-init-manager.ts)
+- [Shared agent package](https://github.com/superset-sh/superset/blob/main/packages/agent/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Squad Tutorial](../claude-squad-tutorial/)
+- [Kilo Code Tutorial](../kilocode-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Setup/Teardown Presets and Automation`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 6: Setup/Teardown Presets and Automation
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `setup`, `superset`, `teardown` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Setup/Teardown Presets and Automation` as an operating subsystem inside **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Setup/Teardown Presets and Automation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `setup`.
+2. **Input normalization**: shape incoming data so `superset` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `teardown`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Superset Repository](https://github.com/superset-sh/superset)
+  Why it matters: authoritative reference on `Superset Repository` (github.com).
+- [Superset README](https://github.com/superset-sh/superset/blob/main/README.md)
+  Why it matters: authoritative reference on `Superset README` (github.com).
+- [Workspace orchestrator](https://github.com/superset-sh/superset/blob/main/apps/cli/src/lib/orchestrators/workspace-orchestrator.ts)
+  Why it matters: authoritative reference on `Workspace orchestrator` (github.com).
+- [Workspace init manager](https://github.com/superset-sh/superset/blob/main/apps/desktop/src/main/lib/workspace-init-manager.ts)
+  Why it matters: authoritative reference on `Workspace init manager` (github.com).
+- [Shared agent package](https://github.com/superset-sh/superset/blob/main/packages/agent/README.md)
+  Why it matters: authoritative reference on `Shared agent package` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `setup` and `superset` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Monitoring, Diff, and Review Workflow](05-monitoring-diff-and-review-workflow.md)
+- [Next Chapter: Chapter 7: Runtime and Package Architecture](07-runtime-and-package-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/superset-terminal-tutorial/07-runtime-and-package-architecture.md b/tutorials/superset-terminal-tutorial/07-runtime-and-package-architecture.md
index 674924b3..4eaabf93 100644
--- a/tutorials/superset-terminal-tutorial/07-runtime-and-package-architecture.md
+++ b/tutorials/superset-terminal-tutorial/07-runtime-and-package-architecture.md
@@ -7,6 +7,9 @@ parent: Superset Terminal Tutorial
 
 # Chapter 7: Runtime and Package Architecture
 
+Welcome to **Chapter 7: Runtime and Package Architecture**. In this part of **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Superset separates desktop runtime concerns and shared agent-execution logic into modular packages.
 
 ## Architecture Surfaces
@@ -27,3 +30,610 @@ Superset separates desktop runtime concerns and shared agent-execution logic int
 You now have a contributor-level map of Superset runtime boundaries.
 
 Next: [Chapter 8: Production Team Operations](08-production-team-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- tutorial slug: **superset-terminal-tutorial**
+- chapter focus: **Chapter 7: Runtime and Package Architecture**
+- system context: **Superset Terminal Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Runtime and Package Architecture`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Superset Repository](https://github.com/superset-sh/superset)
+- [Superset README](https://github.com/superset-sh/superset/blob/main/README.md)
+- [Workspace orchestrator](https://github.com/superset-sh/superset/blob/main/apps/cli/src/lib/orchestrators/workspace-orchestrator.ts)
+- [Workspace init manager](https://github.com/superset-sh/superset/blob/main/apps/desktop/src/main/lib/workspace-init-manager.ts)
+- [Shared agent package](https://github.com/superset-sh/superset/blob/main/packages/agent/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Squad Tutorial](../claude-squad-tutorial/)
+- [Kilo Code Tutorial](../kilocode-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Runtime and Package Architecture`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 7: Runtime and Package Architecture
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Runtime and Package Architecture` as an operating subsystem inside **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Runtime and Package Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Superset Repository](https://github.com/superset-sh/superset)
+  Why it matters: authoritative reference on `Superset Repository` (github.com).
+- [Superset README](https://github.com/superset-sh/superset/blob/main/README.md)
+  Why it matters: authoritative reference on `Superset README` (github.com).
+- [Workspace orchestrator](https://github.com/superset-sh/superset/blob/main/apps/cli/src/lib/orchestrators/workspace-orchestrator.ts)
+  Why it matters: authoritative reference on `Workspace orchestrator` (github.com).
+- [Workspace init manager](https://github.com/superset-sh/superset/blob/main/apps/desktop/src/main/lib/workspace-init-manager.ts)
+  Why it matters: authoritative reference on `Workspace init manager` (github.com).
+- [Shared agent package](https://github.com/superset-sh/superset/blob/main/packages/agent/README.md)
+  Why it matters: authoritative reference on `Shared agent package` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Setup/Teardown Presets and Automation](06-setup-teardown-presets-and-automation.md)
+- [Next Chapter: Chapter 8: Production Team Operations](08-production-team-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/superset-terminal-tutorial/08-production-team-operations.md b/tutorials/superset-terminal-tutorial/08-production-team-operations.md
index d13589a7..50c07d10 100644
--- a/tutorials/superset-terminal-tutorial/08-production-team-operations.md
+++ b/tutorials/superset-terminal-tutorial/08-production-team-operations.md
@@ -7,6 +7,9 @@ parent: Superset Terminal Tutorial
 
 # Chapter 8: Production Team Operations
 
+Welcome to **Chapter 8: Production Team Operations**. In this part of **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Team adoption of Superset needs explicit standards for workspace ownership, quality gates, and agent policy.
 
 ## Team Checklist
@@ -25,3 +28,609 @@ Team adoption of Superset needs explicit standards for workspace ownership, qual
 ## Summary
 
 You now have an operations baseline for running Superset as a team-scale multi-agent command center.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- tutorial slug: **superset-terminal-tutorial**
+- chapter focus: **Chapter 8: Production Team Operations**
+- system context: **Superset Terminal Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Team Operations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Superset Repository](https://github.com/superset-sh/superset)
+- [Superset README](https://github.com/superset-sh/superset/blob/main/README.md)
+- [Workspace orchestrator](https://github.com/superset-sh/superset/blob/main/apps/cli/src/lib/orchestrators/workspace-orchestrator.ts)
+- [Workspace init manager](https://github.com/superset-sh/superset/blob/main/apps/desktop/src/main/lib/workspace-init-manager.ts)
+- [Shared agent package](https://github.com/superset-sh/superset/blob/main/packages/agent/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Squad Tutorial](../claude-squad-tutorial/)
+- [Kilo Code Tutorial](../kilocode-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Team Operations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 8: Production Team Operations
+
+- tutorial context: **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Team Operations` as an operating subsystem inside **Superset Terminal Tutorial: Command Center for Parallel Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Team Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Superset Repository](https://github.com/superset-sh/superset)
+  Why it matters: authoritative reference on `Superset Repository` (github.com).
+- [Superset README](https://github.com/superset-sh/superset/blob/main/README.md)
+  Why it matters: authoritative reference on `Superset README` (github.com).
+- [Workspace orchestrator](https://github.com/superset-sh/superset/blob/main/apps/cli/src/lib/orchestrators/workspace-orchestrator.ts)
+  Why it matters: authoritative reference on `Workspace orchestrator` (github.com).
+- [Workspace init manager](https://github.com/superset-sh/superset/blob/main/apps/desktop/src/main/lib/workspace-init-manager.ts)
+  Why it matters: authoritative reference on `Workspace init manager` (github.com).
+- [Shared agent package](https://github.com/superset-sh/superset/blob/main/packages/agent/README.md)
+  Why it matters: authoritative reference on `Shared agent package` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Runtime and Package Architecture](07-runtime-and-package-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/swarm-tutorial/01-getting-started.md b/tutorials/swarm-tutorial/01-getting-started.md
index 04763b5d..69d6aee8 100644
--- a/tutorials/swarm-tutorial/01-getting-started.md
+++ b/tutorials/swarm-tutorial/01-getting-started.md
@@ -299,3 +299,50 @@ In [Chapter 2: Agent Design](02-agent-design.md), we'll dive deeper into:
 4. Add error handling to your function implementations
 
 *Ready to design professional agents? Continue to [Chapter 2](02-agent-design.md)!*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `agent`, `Agent`, `messages` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with OpenAI Swarm` as an operating subsystem inside **OpenAI Swarm Tutorial: Lightweight Multi-Agent Orchestration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `response`, `instructions`, `client` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with OpenAI Swarm` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `agent`.
+2. **Input normalization**: shape incoming data so `Agent` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `messages`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/openai/swarm)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `agent` and `Agent` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Agent Design](02-agent-design.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/swarm-tutorial/02-agent-design.md b/tutorials/swarm-tutorial/02-agent-design.md
index 9193fc20..c4395968 100644
--- a/tutorials/swarm-tutorial/02-agent-design.md
+++ b/tutorials/swarm-tutorial/02-agent-design.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Agent Design
 
+Welcome to **Chapter 2: Agent Design**. In this part of **OpenAI Swarm Tutorial: Lightweight Multi-Agent Orchestration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In this chapter, you will learn how to design Swarm agents with clear instructions, distinct personas, and well-defined behaviors. Good agent design is the foundation of every effective multi-agent system.
 
 ## What Makes a Good Agent?
@@ -498,3 +501,51 @@ In [Chapter 3: Function Calling & Tools](03-function-calling.md), you will learn
 4. Refactor a "do-everything" agent into three single-purpose agents with appropriate handoffs.
 
 *Built with insights from the [OpenAI Swarm](https://github.com/openai/swarm) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Agent`, `name`, `instructions` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Agent Design` as an operating subsystem inside **OpenAI Swarm Tutorial: Lightweight Multi-Agent Orchestration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `agent`, `transfer`, `support` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Agent Design` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Agent`.
+2. **Input normalization**: shape incoming data so `name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `instructions`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/openai/swarm)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Agent` and `name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with OpenAI Swarm](01-getting-started.md)
+- [Next Chapter: Chapter 3: Function Calling & Tools](03-function-calling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/swarm-tutorial/03-function-calling.md b/tutorials/swarm-tutorial/03-function-calling.md
index 08c36e80..b299da3f 100644
--- a/tutorials/swarm-tutorial/03-function-calling.md
+++ b/tutorials/swarm-tutorial/03-function-calling.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Function Calling & Tools
 
+Welcome to **Chapter 3: Function Calling & Tools**. In this part of **OpenAI Swarm Tutorial: Lightweight Multi-Agent Orchestration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In this chapter, you will learn how to equip Swarm agents with real capabilities by defining Python functions they can call as tools. Function calling is what transforms an agent from a chatbot into an actionable assistant.
 
 ## How Function Calling Works in Swarm
@@ -577,3 +580,51 @@ In [Chapter 4: Routines](04-routines.md), you will learn how to:
 4. Design a tool set for a project management agent (create tasks, assign owners, update status, list tasks).
 
 *Built with insights from the [OpenAI Swarm](https://github.com/openai/swarm) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `order`, `amount`, `Error` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Function Calling & Tools` as an operating subsystem inside **OpenAI Swarm Tutorial: Lightweight Multi-Agent Orchestration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `content`, `json`, `product_id` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Function Calling & Tools` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `order`.
+2. **Input normalization**: shape incoming data so `amount` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Error`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/openai/swarm)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `order` and `amount` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Agent Design](02-agent-design.md)
+- [Next Chapter: Chapter 4: Routines](04-routines.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/swarm-tutorial/04-routines.md b/tutorials/swarm-tutorial/04-routines.md
index 1f035e95..29c3401a 100644
--- a/tutorials/swarm-tutorial/04-routines.md
+++ b/tutorials/swarm-tutorial/04-routines.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Routines
 
+Welcome to **Chapter 4: Routines**. In this part of **OpenAI Swarm Tutorial: Lightweight Multi-Agent Orchestration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In this chapter, you will learn how to build routines -- multi-step workflows that guide agents through a predictable sequence of actions. Routines are the Swarm equivalent of standard operating procedures: they turn free-form conversations into structured, repeatable processes.
 
 ## What is a Routine?
@@ -605,3 +608,51 @@ In [Chapter 5: Agent Handoffs](05-handoffs.md), you will learn how to:
 4. Chain three agents together, each running a different routine segment in a support pipeline.
 
 *Built with insights from the [OpenAI Swarm](https://github.com/openai/swarm) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Step`, `account`, `Agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Routines` as an operating subsystem inside **OpenAI Swarm Tutorial: Lightweight Multi-Agent Orchestration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `name`, `account_id`, `customer` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Routines` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Step`.
+2. **Input normalization**: shape incoming data so `account` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/openai/swarm)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Step` and `account` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Function Calling & Tools](03-function-calling.md)
+- [Next Chapter: Chapter 5: Agent Handoffs](05-handoffs.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/swarm-tutorial/05-handoffs.md b/tutorials/swarm-tutorial/05-handoffs.md
index bcdfffa5..22c0ddde 100644
--- a/tutorials/swarm-tutorial/05-handoffs.md
+++ b/tutorials/swarm-tutorial/05-handoffs.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Agent Handoffs
 
+Welcome to **Chapter 5: Agent Handoffs**. In this part of **OpenAI Swarm Tutorial: Lightweight Multi-Agent Orchestration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In this chapter, you will learn how to implement seamless control transfers between agents. Handoffs are the core mechanism that makes Swarm a multi-agent framework -- they allow specialized agents to collaborate on a single conversation.
 
 ## What is a Handoff?
@@ -663,3 +666,51 @@ In [Chapter 6: Context Variables](06-context-variables.md), you will learn how t
 4. Create a handoff loop detector that logs a warning after 3 transfers in a single session.
 
 *Built with insights from the [OpenAI Swarm](https://github.com/openai/swarm) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Agent`, `name`, `agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Agent Handoffs` as an operating subsystem inside **OpenAI Swarm Tutorial: Lightweight Multi-Agent Orchestration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `billing`, `Result`, `context_variables` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Agent Handoffs` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Agent`.
+2. **Input normalization**: shape incoming data so `name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/openai/swarm)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Agent` and `name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Routines](04-routines.md)
+- [Next Chapter: Chapter 6: Context Variables](06-context-variables.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/swarm-tutorial/06-context-variables.md b/tutorials/swarm-tutorial/06-context-variables.md
index cc05ad26..c31e34f8 100644
--- a/tutorials/swarm-tutorial/06-context-variables.md
+++ b/tutorials/swarm-tutorial/06-context-variables.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Context Variables
 
+Welcome to **Chapter 6: Context Variables**. In this part of **OpenAI Swarm Tutorial: Lightweight Multi-Agent Orchestration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In this chapter, you will learn how to manage shared state across agents using context variables. Context variables are the memory of your multi-agent system -- they carry information between handoffs, personalize agent behavior, and track the state of an ongoing interaction.
 
 ## What are Context Variables?
@@ -599,3 +602,51 @@ In [Chapter 7: Multi-Agent Patterns](07-multi-agent-patterns.md), you will learn
 4. Write a context debugging tool that tracks and compares snapshots across a conversation.
 
 *Built with insights from the [OpenAI Swarm](https://github.com/openai/swarm) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `context_variables`, `Result`, `name` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Context Variables` as an operating subsystem inside **OpenAI Swarm Tutorial: Lightweight Multi-Agent Orchestration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `context`, `Agent`, `account_id` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Context Variables` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `context_variables`.
+2. **Input normalization**: shape incoming data so `Result` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `name`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/openai/swarm)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `context_variables` and `Result` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Agent Handoffs](05-handoffs.md)
+- [Next Chapter: Chapter 7: Multi-Agent Patterns](07-multi-agent-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/swarm-tutorial/07-multi-agent-patterns.md b/tutorials/swarm-tutorial/07-multi-agent-patterns.md
index 6ebef0ed..072eeda9 100644
--- a/tutorials/swarm-tutorial/07-multi-agent-patterns.md
+++ b/tutorials/swarm-tutorial/07-multi-agent-patterns.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Multi-Agent Patterns
 
+Welcome to **Chapter 7: Multi-Agent Patterns**. In this part of **OpenAI Swarm Tutorial: Lightweight Multi-Agent Orchestration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In this chapter, you will learn how to combine agents using proven orchestration patterns for complex tasks. These patterns go beyond simple triage routing, enabling sophisticated workflows like planning-execution loops, parallel analysis, and consensus-based decision making.
 
 ## Why Patterns Matter
@@ -956,3 +959,51 @@ In [Chapter 8: Production Considerations](08-production.md), you will learn how
 4. Combine a triage router with a review loop: simple requests go direct, complex requests go through the review cycle.
 
 *Built with insights from the [OpenAI Swarm](https://github.com/openai/swarm) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `context_variables`, `Agent`, `Result` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Multi-Agent Patterns` as an operating subsystem inside **OpenAI Swarm Tutorial: Lightweight Multi-Agent Orchestration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `name`, `instructions`, `task` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Multi-Agent Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `context_variables`.
+2. **Input normalization**: shape incoming data so `Agent` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Result`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/openai/swarm)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `context_variables` and `Agent` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Context Variables](06-context-variables.md)
+- [Next Chapter: Chapter 8: Production Considerations](08-production.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/swarm-tutorial/08-production.md b/tutorials/swarm-tutorial/08-production.md
index bcb3f5fc..6ccab474 100644
--- a/tutorials/swarm-tutorial/08-production.md
+++ b/tutorials/swarm-tutorial/08-production.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Production Considerations
 
+Welcome to **Chapter 8: Production Considerations**. In this part of **OpenAI Swarm Tutorial: Lightweight Multi-Agent Orchestration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In this chapter, you will learn how to take Swarm agent systems from prototype to production. We cover observability, safety guardrails, cost management, error handling, and operational best practices for running multi-agent systems reliably.
 
 > **Note**: Swarm is an experimental/educational framework. For production deployments, you will likely build on Swarm's patterns rather than using the library directly. The principles in this chapter apply regardless of framework.
@@ -780,3 +783,50 @@ You have completed the OpenAI Swarm tutorial. Here are paths for continued learn
 5. Write a test suite that validates all handoff paths in a three-agent triage system.
 
 *Built with insights from the [OpenAI Swarm](https://github.com/openai/swarm) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `messages`, `agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Considerations` as an operating subsystem inside **OpenAI Swarm Tutorial: Lightweight Multi-Agent Orchestration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `context_variables`, `response`, `time` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Considerations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `messages` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/openai/swarm)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `messages` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Multi-Agent Patterns](07-multi-agent-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/swe-agent-tutorial/01-getting-started.md b/tutorials/swe-agent-tutorial/01-getting-started.md
index 9d2495f0..95ec48dc 100644
--- a/tutorials/swe-agent-tutorial/01-getting-started.md
+++ b/tutorials/swe-agent-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: SWE-agent Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets SWE-agent running on a first issue-resolution workflow.
 
 ## Learning Goals
@@ -34,3 +37,600 @@ This chapter gets SWE-agent running on a first issue-resolution workflow.
 You now have a working SWE-agent baseline and can execute initial issue workflows.
 
 Next: [Chapter 2: Core Architecture and YAML Configuration](02-core-architecture-and-yaml-configuration.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- tutorial slug: **swe-agent-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Swe Agent Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [SWE-agent Repository](https://github.com/SWE-agent/SWE-agent)
+- [SWE-agent README](https://github.com/SWE-agent/SWE-agent/blob/main/README.md)
+- [SWE-agent Docs](https://swe-agent.com/latest/)
+- [Hello World Usage](https://swe-agent.com/latest/usage/hello_world/)
+- [Batch Mode Usage](https://swe-agent.com/latest/usage/batch_mode/)
+- [Development Contribution Docs](https://swe-agent.com/latest/dev/contribute/)
+
+### Cross-Tutorial Connection Map
+
+- [Open SWE Tutorial](../open-swe-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [SWE-agent Repository](https://github.com/SWE-agent/SWE-agent)
+  Why it matters: authoritative reference on `SWE-agent Repository` (github.com).
+- [SWE-agent README](https://github.com/SWE-agent/SWE-agent/blob/main/README.md)
+  Why it matters: authoritative reference on `SWE-agent README` (github.com).
+- [SWE-agent Docs](https://swe-agent.com/latest/)
+  Why it matters: authoritative reference on `SWE-agent Docs` (swe-agent.com).
+- [Hello World Usage](https://swe-agent.com/latest/usage/hello_world/)
+  Why it matters: authoritative reference on `Hello World Usage` (swe-agent.com).
+- [Batch Mode Usage](https://swe-agent.com/latest/usage/batch_mode/)
+  Why it matters: authoritative reference on `Batch Mode Usage` (swe-agent.com).
+- [Development Contribution Docs](https://swe-agent.com/latest/dev/contribute/)
+  Why it matters: authoritative reference on `Development Contribution Docs` (swe-agent.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Core Architecture and YAML Configuration](02-core-architecture-and-yaml-configuration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/swe-agent-tutorial/02-core-architecture-and-yaml-configuration.md b/tutorials/swe-agent-tutorial/02-core-architecture-and-yaml-configuration.md
index 2c0034e2..72cd7cd1 100644
--- a/tutorials/swe-agent-tutorial/02-core-architecture-and-yaml-configuration.md
+++ b/tutorials/swe-agent-tutorial/02-core-architecture-and-yaml-configuration.md
@@ -7,6 +7,9 @@ parent: SWE-agent Tutorial
 
 # Chapter 2: Core Architecture and YAML Configuration
 
+Welcome to **Chapter 2: Core Architecture and YAML Configuration**. In this part of **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains SWE-agent's configuration-first design centered around YAML files.
 
 ## Learning Goals
@@ -34,3 +37,601 @@ This chapter explains SWE-agent's configuration-first design centered around YAM
 You now understand the key control points for predictable SWE-agent behavior.
 
 Next: [Chapter 3: CLI Workflows and Usage Modes](03-cli-workflows-and-usage-modes.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- tutorial slug: **swe-agent-tutorial**
+- chapter focus: **Chapter 2: Core Architecture and YAML Configuration**
+- system context: **Swe Agent Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Core Architecture and YAML Configuration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [SWE-agent Repository](https://github.com/SWE-agent/SWE-agent)
+- [SWE-agent README](https://github.com/SWE-agent/SWE-agent/blob/main/README.md)
+- [SWE-agent Docs](https://swe-agent.com/latest/)
+- [Hello World Usage](https://swe-agent.com/latest/usage/hello_world/)
+- [Batch Mode Usage](https://swe-agent.com/latest/usage/batch_mode/)
+- [Development Contribution Docs](https://swe-agent.com/latest/dev/contribute/)
+
+### Cross-Tutorial Connection Map
+
+- [Open SWE Tutorial](../open-swe-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Core Architecture and YAML Configuration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Core Architecture and YAML Configuration
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Core Architecture and YAML Configuration` as an operating subsystem inside **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Core Architecture and YAML Configuration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [SWE-agent Repository](https://github.com/SWE-agent/SWE-agent)
+  Why it matters: authoritative reference on `SWE-agent Repository` (github.com).
+- [SWE-agent README](https://github.com/SWE-agent/SWE-agent/blob/main/README.md)
+  Why it matters: authoritative reference on `SWE-agent README` (github.com).
+- [SWE-agent Docs](https://swe-agent.com/latest/)
+  Why it matters: authoritative reference on `SWE-agent Docs` (swe-agent.com).
+- [Hello World Usage](https://swe-agent.com/latest/usage/hello_world/)
+  Why it matters: authoritative reference on `Hello World Usage` (swe-agent.com).
+- [Batch Mode Usage](https://swe-agent.com/latest/usage/batch_mode/)
+  Why it matters: authoritative reference on `Batch Mode Usage` (swe-agent.com).
+- [Development Contribution Docs](https://swe-agent.com/latest/dev/contribute/)
+  Why it matters: authoritative reference on `Development Contribution Docs` (swe-agent.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: CLI Workflows and Usage Modes](03-cli-workflows-and-usage-modes.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/swe-agent-tutorial/03-cli-workflows-and-usage-modes.md b/tutorials/swe-agent-tutorial/03-cli-workflows-and-usage-modes.md
index 1e18db49..3d59b757 100644
--- a/tutorials/swe-agent-tutorial/03-cli-workflows-and-usage-modes.md
+++ b/tutorials/swe-agent-tutorial/03-cli-workflows-and-usage-modes.md
@@ -7,6 +7,9 @@ parent: SWE-agent Tutorial
 
 # Chapter 3: CLI Workflows and Usage Modes
 
+Welcome to **Chapter 3: CLI Workflows and Usage Modes**. In this part of **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers how to move between single-run and batch workflows.
 
 ## Learning Goals
@@ -33,3 +36,601 @@ This chapter covers how to move between single-run and batch workflows.
 You can now choose the right execution mode for local debugging or scale evaluation.
 
 Next: [Chapter 4: Tooling, Environments, and Model Strategy](04-tooling-environments-and-model-strategy.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- tutorial slug: **swe-agent-tutorial**
+- chapter focus: **Chapter 3: CLI Workflows and Usage Modes**
+- system context: **Swe Agent Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: CLI Workflows and Usage Modes`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [SWE-agent Repository](https://github.com/SWE-agent/SWE-agent)
+- [SWE-agent README](https://github.com/SWE-agent/SWE-agent/blob/main/README.md)
+- [SWE-agent Docs](https://swe-agent.com/latest/)
+- [Hello World Usage](https://swe-agent.com/latest/usage/hello_world/)
+- [Batch Mode Usage](https://swe-agent.com/latest/usage/batch_mode/)
+- [Development Contribution Docs](https://swe-agent.com/latest/dev/contribute/)
+
+### Cross-Tutorial Connection Map
+
+- [Open SWE Tutorial](../open-swe-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: CLI Workflows and Usage Modes`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: CLI Workflows and Usage Modes
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: CLI Workflows and Usage Modes` as an operating subsystem inside **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: CLI Workflows and Usage Modes` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [SWE-agent Repository](https://github.com/SWE-agent/SWE-agent)
+  Why it matters: authoritative reference on `SWE-agent Repository` (github.com).
+- [SWE-agent README](https://github.com/SWE-agent/SWE-agent/blob/main/README.md)
+  Why it matters: authoritative reference on `SWE-agent README` (github.com).
+- [SWE-agent Docs](https://swe-agent.com/latest/)
+  Why it matters: authoritative reference on `SWE-agent Docs` (swe-agent.com).
+- [Hello World Usage](https://swe-agent.com/latest/usage/hello_world/)
+  Why it matters: authoritative reference on `Hello World Usage` (swe-agent.com).
+- [Batch Mode Usage](https://swe-agent.com/latest/usage/batch_mode/)
+  Why it matters: authoritative reference on `Batch Mode Usage` (swe-agent.com).
+- [Development Contribution Docs](https://swe-agent.com/latest/dev/contribute/)
+  Why it matters: authoritative reference on `Development Contribution Docs` (swe-agent.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Core Architecture and YAML Configuration](02-core-architecture-and-yaml-configuration.md)
+- [Next Chapter: Chapter 4: Tooling, Environments, and Model Strategy](04-tooling-environments-and-model-strategy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/swe-agent-tutorial/04-tooling-environments-and-model-strategy.md b/tutorials/swe-agent-tutorial/04-tooling-environments-and-model-strategy.md
index 5e1ffd19..3c7b7e0a 100644
--- a/tutorials/swe-agent-tutorial/04-tooling-environments-and-model-strategy.md
+++ b/tutorials/swe-agent-tutorial/04-tooling-environments-and-model-strategy.md
@@ -7,6 +7,9 @@ parent: SWE-agent Tutorial
 
 # Chapter 4: Tooling, Environments, and Model Strategy
 
+Welcome to **Chapter 4: Tooling, Environments, and Model Strategy**. In this part of **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on runtime controls that materially affect quality and cost.
 
 ## Learning Goals
@@ -34,3 +37,601 @@ This chapter focuses on runtime controls that materially affect quality and cost
 You now have a strategy for balancing reliability, cost, and speed in SWE-agent runs.
 
 Next: [Chapter 5: Benchmarking and Evaluation Practices](05-benchmarking-and-evaluation-practices.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- tutorial slug: **swe-agent-tutorial**
+- chapter focus: **Chapter 4: Tooling, Environments, and Model Strategy**
+- system context: **Swe Agent Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Tooling, Environments, and Model Strategy`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [SWE-agent Repository](https://github.com/SWE-agent/SWE-agent)
+- [SWE-agent README](https://github.com/SWE-agent/SWE-agent/blob/main/README.md)
+- [SWE-agent Docs](https://swe-agent.com/latest/)
+- [Hello World Usage](https://swe-agent.com/latest/usage/hello_world/)
+- [Batch Mode Usage](https://swe-agent.com/latest/usage/batch_mode/)
+- [Development Contribution Docs](https://swe-agent.com/latest/dev/contribute/)
+
+### Cross-Tutorial Connection Map
+
+- [Open SWE Tutorial](../open-swe-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Tooling, Environments, and Model Strategy`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Tooling, Environments, and Model Strategy
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Tooling, Environments, and Model Strategy` as an operating subsystem inside **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Tooling, Environments, and Model Strategy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [SWE-agent Repository](https://github.com/SWE-agent/SWE-agent)
+  Why it matters: authoritative reference on `SWE-agent Repository` (github.com).
+- [SWE-agent README](https://github.com/SWE-agent/SWE-agent/blob/main/README.md)
+  Why it matters: authoritative reference on `SWE-agent README` (github.com).
+- [SWE-agent Docs](https://swe-agent.com/latest/)
+  Why it matters: authoritative reference on `SWE-agent Docs` (swe-agent.com).
+- [Hello World Usage](https://swe-agent.com/latest/usage/hello_world/)
+  Why it matters: authoritative reference on `Hello World Usage` (swe-agent.com).
+- [Batch Mode Usage](https://swe-agent.com/latest/usage/batch_mode/)
+  Why it matters: authoritative reference on `Batch Mode Usage` (swe-agent.com).
+- [Development Contribution Docs](https://swe-agent.com/latest/dev/contribute/)
+  Why it matters: authoritative reference on `Development Contribution Docs` (swe-agent.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: CLI Workflows and Usage Modes](03-cli-workflows-and-usage-modes.md)
+- [Next Chapter: Chapter 5: Benchmarking and Evaluation Practices](05-benchmarking-and-evaluation-practices.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/swe-agent-tutorial/05-benchmarking-and-evaluation-practices.md b/tutorials/swe-agent-tutorial/05-benchmarking-and-evaluation-practices.md
index 014a2d6d..d1bd4119 100644
--- a/tutorials/swe-agent-tutorial/05-benchmarking-and-evaluation-practices.md
+++ b/tutorials/swe-agent-tutorial/05-benchmarking-and-evaluation-practices.md
@@ -7,6 +7,9 @@ parent: SWE-agent Tutorial
 
 # Chapter 5: Benchmarking and Evaluation Practices
 
+Welcome to **Chapter 5: Benchmarking and Evaluation Practices**. In this part of **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter maps SWE-agent usage to benchmark-grade evaluation habits.
 
 ## Learning Goals
@@ -34,3 +37,601 @@ This chapter maps SWE-agent usage to benchmark-grade evaluation habits.
 You now have a repeatable framework for benchmarking SWE-agent systems.
 
 Next: [Chapter 6: Offensive Security Mode and Specialized Workloads](06-offensive-security-mode-and-specialized-workloads.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- tutorial slug: **swe-agent-tutorial**
+- chapter focus: **Chapter 5: Benchmarking and Evaluation Practices**
+- system context: **Swe Agent Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Benchmarking and Evaluation Practices`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [SWE-agent Repository](https://github.com/SWE-agent/SWE-agent)
+- [SWE-agent README](https://github.com/SWE-agent/SWE-agent/blob/main/README.md)
+- [SWE-agent Docs](https://swe-agent.com/latest/)
+- [Hello World Usage](https://swe-agent.com/latest/usage/hello_world/)
+- [Batch Mode Usage](https://swe-agent.com/latest/usage/batch_mode/)
+- [Development Contribution Docs](https://swe-agent.com/latest/dev/contribute/)
+
+### Cross-Tutorial Connection Map
+
+- [Open SWE Tutorial](../open-swe-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Benchmarking and Evaluation Practices`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Benchmarking and Evaluation Practices
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Benchmarking and Evaluation Practices` as an operating subsystem inside **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Benchmarking and Evaluation Practices` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [SWE-agent Repository](https://github.com/SWE-agent/SWE-agent)
+  Why it matters: authoritative reference on `SWE-agent Repository` (github.com).
+- [SWE-agent README](https://github.com/SWE-agent/SWE-agent/blob/main/README.md)
+  Why it matters: authoritative reference on `SWE-agent README` (github.com).
+- [SWE-agent Docs](https://swe-agent.com/latest/)
+  Why it matters: authoritative reference on `SWE-agent Docs` (swe-agent.com).
+- [Hello World Usage](https://swe-agent.com/latest/usage/hello_world/)
+  Why it matters: authoritative reference on `Hello World Usage` (swe-agent.com).
+- [Batch Mode Usage](https://swe-agent.com/latest/usage/batch_mode/)
+  Why it matters: authoritative reference on `Batch Mode Usage` (swe-agent.com).
+- [Development Contribution Docs](https://swe-agent.com/latest/dev/contribute/)
+  Why it matters: authoritative reference on `Development Contribution Docs` (swe-agent.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Tooling, Environments, and Model Strategy](04-tooling-environments-and-model-strategy.md)
+- [Next Chapter: Chapter 6: Offensive Security Mode and Specialized Workloads](06-offensive-security-mode-and-specialized-workloads.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/swe-agent-tutorial/06-offensive-security-mode-and-specialized-workloads.md b/tutorials/swe-agent-tutorial/06-offensive-security-mode-and-specialized-workloads.md
index ae261bd8..eaaf36af 100644
--- a/tutorials/swe-agent-tutorial/06-offensive-security-mode-and-specialized-workloads.md
+++ b/tutorials/swe-agent-tutorial/06-offensive-security-mode-and-specialized-workloads.md
@@ -7,6 +7,9 @@ parent: SWE-agent Tutorial
 
 # Chapter 6: Offensive Security Mode and Specialized Workloads
 
+Welcome to **Chapter 6: Offensive Security Mode and Specialized Workloads**. In this part of **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains workload specialization and when to use security-focused variants.
 
 ## Learning Goals
@@ -31,3 +34,613 @@ SWE-agent's EnIGMA path targets offensive cybersecurity challenge workflows. Kee
 You now understand how specialized security workloads fit into the broader SWE-agent ecosystem.
 
 Next: [Chapter 7: Development and Contribution Workflow](07-development-and-contribution-workflow.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- tutorial slug: **swe-agent-tutorial**
+- chapter focus: **Chapter 6: Offensive Security Mode and Specialized Workloads**
+- system context: **Swe Agent Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Offensive Security Mode and Specialized Workloads`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [SWE-agent Repository](https://github.com/SWE-agent/SWE-agent)
+- [SWE-agent README](https://github.com/SWE-agent/SWE-agent/blob/main/README.md)
+- [SWE-agent Docs](https://swe-agent.com/latest/)
+- [Hello World Usage](https://swe-agent.com/latest/usage/hello_world/)
+- [Batch Mode Usage](https://swe-agent.com/latest/usage/batch_mode/)
+- [Development Contribution Docs](https://swe-agent.com/latest/dev/contribute/)
+
+### Cross-Tutorial Connection Map
+
+- [Open SWE Tutorial](../open-swe-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Offensive Security Mode and Specialized Workloads`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 6: Offensive Security Mode and Specialized Workloads
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Offensive Security Mode and Specialized Workloads` as an operating subsystem inside **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Offensive Security Mode and Specialized Workloads` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [SWE-agent Repository](https://github.com/SWE-agent/SWE-agent)
+  Why it matters: authoritative reference on `SWE-agent Repository` (github.com).
+- [SWE-agent README](https://github.com/SWE-agent/SWE-agent/blob/main/README.md)
+  Why it matters: authoritative reference on `SWE-agent README` (github.com).
+- [SWE-agent Docs](https://swe-agent.com/latest/)
+  Why it matters: authoritative reference on `SWE-agent Docs` (swe-agent.com).
+- [Hello World Usage](https://swe-agent.com/latest/usage/hello_world/)
+  Why it matters: authoritative reference on `Hello World Usage` (swe-agent.com).
+- [Batch Mode Usage](https://swe-agent.com/latest/usage/batch_mode/)
+  Why it matters: authoritative reference on `Batch Mode Usage` (swe-agent.com).
+- [Development Contribution Docs](https://swe-agent.com/latest/dev/contribute/)
+  Why it matters: authoritative reference on `Development Contribution Docs` (swe-agent.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Benchmarking and Evaluation Practices](05-benchmarking-and-evaluation-practices.md)
+- [Next Chapter: Chapter 7: Development and Contribution Workflow](07-development-and-contribution-workflow.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/swe-agent-tutorial/07-development-and-contribution-workflow.md b/tutorials/swe-agent-tutorial/07-development-and-contribution-workflow.md
index 6e522b03..c5788fa6 100644
--- a/tutorials/swe-agent-tutorial/07-development-and-contribution-workflow.md
+++ b/tutorials/swe-agent-tutorial/07-development-and-contribution-workflow.md
@@ -7,6 +7,9 @@ parent: SWE-agent Tutorial
 
 # Chapter 7: Development and Contribution Workflow
 
+Welcome to **Chapter 7: Development and Contribution Workflow**. In this part of **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers how to contribute effectively while keeping changes reviewable and testable.
 
 ## Learning Goals
@@ -34,3 +37,601 @@ This chapter covers how to contribute effectively while keeping changes reviewab
 You now have a practical contribution workflow aligned with SWE-agent maintainers.
 
 Next: [Chapter 8: Production Operations and Governance](08-production-operations-and-governance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- tutorial slug: **swe-agent-tutorial**
+- chapter focus: **Chapter 7: Development and Contribution Workflow**
+- system context: **Swe Agent Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Development and Contribution Workflow`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [SWE-agent Repository](https://github.com/SWE-agent/SWE-agent)
+- [SWE-agent README](https://github.com/SWE-agent/SWE-agent/blob/main/README.md)
+- [SWE-agent Docs](https://swe-agent.com/latest/)
+- [Hello World Usage](https://swe-agent.com/latest/usage/hello_world/)
+- [Batch Mode Usage](https://swe-agent.com/latest/usage/batch_mode/)
+- [Development Contribution Docs](https://swe-agent.com/latest/dev/contribute/)
+
+### Cross-Tutorial Connection Map
+
+- [Open SWE Tutorial](../open-swe-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Development and Contribution Workflow`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Development and Contribution Workflow
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Development and Contribution Workflow` as an operating subsystem inside **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Development and Contribution Workflow` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [SWE-agent Repository](https://github.com/SWE-agent/SWE-agent)
+  Why it matters: authoritative reference on `SWE-agent Repository` (github.com).
+- [SWE-agent README](https://github.com/SWE-agent/SWE-agent/blob/main/README.md)
+  Why it matters: authoritative reference on `SWE-agent README` (github.com).
+- [SWE-agent Docs](https://swe-agent.com/latest/)
+  Why it matters: authoritative reference on `SWE-agent Docs` (swe-agent.com).
+- [Hello World Usage](https://swe-agent.com/latest/usage/hello_world/)
+  Why it matters: authoritative reference on `Hello World Usage` (swe-agent.com).
+- [Batch Mode Usage](https://swe-agent.com/latest/usage/batch_mode/)
+  Why it matters: authoritative reference on `Batch Mode Usage` (swe-agent.com).
+- [Development Contribution Docs](https://swe-agent.com/latest/dev/contribute/)
+  Why it matters: authoritative reference on `Development Contribution Docs` (swe-agent.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Offensive Security Mode and Specialized Workloads](06-offensive-security-mode-and-specialized-workloads.md)
+- [Next Chapter: Chapter 8: Production Operations and Governance](08-production-operations-and-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/swe-agent-tutorial/08-production-operations-and-governance.md b/tutorials/swe-agent-tutorial/08-production-operations-and-governance.md
index 94a10746..91031549 100644
--- a/tutorials/swe-agent-tutorial/08-production-operations-and-governance.md
+++ b/tutorials/swe-agent-tutorial/08-production-operations-and-governance.md
@@ -7,6 +7,9 @@ parent: SWE-agent Tutorial
 
 # Chapter 8: Production Operations and Governance
 
+Welcome to **Chapter 8: Production Operations and Governance**. In this part of **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter maps production responsibilities for teams using autonomous coding agents.
 
 ## Learning Goals
@@ -34,3 +37,600 @@ This chapter maps production responsibilities for teams using autonomous coding
 You now have a full SWE-agent learning path from setup to production governance.
 
 Next tutorial: [Open SWE Tutorial](../open-swe-tutorial/)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- tutorial slug: **swe-agent-tutorial**
+- chapter focus: **Chapter 8: Production Operations and Governance**
+- system context: **Swe Agent Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Operations and Governance`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [SWE-agent Repository](https://github.com/SWE-agent/SWE-agent)
+- [SWE-agent README](https://github.com/SWE-agent/SWE-agent/blob/main/README.md)
+- [SWE-agent Docs](https://swe-agent.com/latest/)
+- [Hello World Usage](https://swe-agent.com/latest/usage/hello_world/)
+- [Batch Mode Usage](https://swe-agent.com/latest/usage/batch_mode/)
+- [Development Contribution Docs](https://swe-agent.com/latest/dev/contribute/)
+
+### Cross-Tutorial Connection Map
+
+- [Open SWE Tutorial](../open-swe-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [LangGraph Tutorial](../langgraph-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Operations and Governance`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Operations and Governance
+
+- tutorial context: **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Operations and Governance` as an operating subsystem inside **SWE-agent Tutorial: Autonomous Repository Repair and Benchmark-Driven Engineering**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Operations and Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [SWE-agent Repository](https://github.com/SWE-agent/SWE-agent)
+  Why it matters: authoritative reference on `SWE-agent Repository` (github.com).
+- [SWE-agent README](https://github.com/SWE-agent/SWE-agent/blob/main/README.md)
+  Why it matters: authoritative reference on `SWE-agent README` (github.com).
+- [SWE-agent Docs](https://swe-agent.com/latest/)
+  Why it matters: authoritative reference on `SWE-agent Docs` (swe-agent.com).
+- [Hello World Usage](https://swe-agent.com/latest/usage/hello_world/)
+  Why it matters: authoritative reference on `Hello World Usage` (swe-agent.com).
+- [Batch Mode Usage](https://swe-agent.com/latest/usage/batch_mode/)
+  Why it matters: authoritative reference on `Batch Mode Usage` (swe-agent.com).
+- [Development Contribution Docs](https://swe-agent.com/latest/dev/contribute/)
+  Why it matters: authoritative reference on `Development Contribution Docs` (swe-agent.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Development and Contribution Workflow](07-development-and-contribution-workflow.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/sweep-tutorial/01-getting-started-and-current-product-posture.md b/tutorials/sweep-tutorial/01-getting-started-and-current-product-posture.md
index 2d495143..d25417fd 100644
--- a/tutorials/sweep-tutorial/01-getting-started-and-current-product-posture.md
+++ b/tutorials/sweep-tutorial/01-getting-started-and-current-product-posture.md
@@ -7,6 +7,9 @@ parent: Sweep Tutorial
 
 # Chapter 1: Getting Started and Current Product Posture
 
+Welcome to **Chapter 1: Getting Started and Current Product Posture**. In this part of **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter establishes where Sweep stands today and how to choose a practical adoption entry point.
 
 ## Learning Goals
@@ -47,3 +50,594 @@ This chapter establishes where Sweep stands today and how to choose a practical
 You now have a realistic starting context and first execution path.
 
 Next: [Chapter 2: Issue to PR Workflow Architecture](02-issue-to-pr-workflow-architecture.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- tutorial slug: **sweep-tutorial**
+- chapter focus: **Chapter 1: Getting Started and Current Product Posture**
+- system context: **Sweep Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started and Current Product Posture`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Sweep Repository](https://github.com/sweepai/sweep)
+- [README](https://github.com/sweepai/sweep/blob/main/README.md)
+- [Docs Home](https://docs.sweep.dev/)
+- [Getting Started](https://github.com/sweepai/sweep/blob/main/docs/pages/getting-started.md)
+- [Config](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/config.mdx)
+- [Advanced Usage](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/advanced.mdx)
+- [CLI](https://github.com/sweepai/sweep/blob/main/docs/pages/cli.mdx)
+- [Deployment](https://github.com/sweepai/sweep/blob/main/docs/pages/deployment.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Tabby Tutorial](../tabby-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Stagewise Tutorial](../stagewise-tutorial/)
+- [Chapter 1: Getting Started and Current Product Posture](01-getting-started-and-current-product-posture.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started and Current Product Posture`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started and Current Product Posture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started and Current Product Posture` as an operating subsystem inside **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started and Current Product Posture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Sweep Repository](https://github.com/sweepai/sweep)
+  Why it matters: authoritative reference on `Sweep Repository` (github.com).
+- [README](https://github.com/sweepai/sweep/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Docs Home](https://docs.sweep.dev/)
+  Why it matters: authoritative reference on `Docs Home` (docs.sweep.dev).
+- [Getting Started](https://github.com/sweepai/sweep/blob/main/docs/pages/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Config](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/config.mdx)
+  Why it matters: authoritative reference on `Config` (github.com).
+- [Advanced Usage](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/advanced.mdx)
+  Why it matters: authoritative reference on `Advanced Usage` (github.com).
+- [CLI](https://github.com/sweepai/sweep/blob/main/docs/pages/cli.mdx)
+  Why it matters: authoritative reference on `CLI` (github.com).
+- [Deployment](https://github.com/sweepai/sweep/blob/main/docs/pages/deployment.mdx)
+  Why it matters: authoritative reference on `Deployment` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Issue to PR Workflow Architecture](02-issue-to-pr-workflow-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/sweep-tutorial/02-issue-to-pr-workflow-architecture.md b/tutorials/sweep-tutorial/02-issue-to-pr-workflow-architecture.md
index 7ca0bea7..bef077e7 100644
--- a/tutorials/sweep-tutorial/02-issue-to-pr-workflow-architecture.md
+++ b/tutorials/sweep-tutorial/02-issue-to-pr-workflow-architecture.md
@@ -7,6 +7,9 @@ parent: Sweep Tutorial
 
 # Chapter 2: Issue to PR Workflow Architecture
 
+Welcome to **Chapter 2: Issue to PR Workflow Architecture**. In this part of **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Sweep is built around asynchronous task execution from issue intake to PR generation.
 
 ## Learning Goals
@@ -52,3 +55,599 @@ sequenceDiagram
 You now have a lifecycle map for how Sweep executes issue-driven coding work.
 
 Next: [Chapter 3: Repository Configuration and Governance](03-repository-configuration-and-governance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- tutorial slug: **sweep-tutorial**
+- chapter focus: **Chapter 2: Issue to PR Workflow Architecture**
+- system context: **Sweep Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Issue to PR Workflow Architecture`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Sweep Repository](https://github.com/sweepai/sweep)
+- [README](https://github.com/sweepai/sweep/blob/main/README.md)
+- [Docs Home](https://docs.sweep.dev/)
+- [Getting Started](https://github.com/sweepai/sweep/blob/main/docs/pages/getting-started.md)
+- [Config](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/config.mdx)
+- [Advanced Usage](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/advanced.mdx)
+- [CLI](https://github.com/sweepai/sweep/blob/main/docs/pages/cli.mdx)
+- [Deployment](https://github.com/sweepai/sweep/blob/main/docs/pages/deployment.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Tabby Tutorial](../tabby-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Stagewise Tutorial](../stagewise-tutorial/)
+- [Chapter 1: Getting Started and Current Product Posture](01-getting-started-and-current-product-posture.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Issue to PR Workflow Architecture`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Issue to PR Workflow Architecture
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Sweep`, `participant`, `User` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Issue to PR Workflow Architecture` as an operating subsystem inside **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Repo`, `event`, `update` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Issue to PR Workflow Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Sweep`.
+2. **Input normalization**: shape incoming data so `participant` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `User`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Sweep Repository](https://github.com/sweepai/sweep)
+  Why it matters: authoritative reference on `Sweep Repository` (github.com).
+- [README](https://github.com/sweepai/sweep/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Docs Home](https://docs.sweep.dev/)
+  Why it matters: authoritative reference on `Docs Home` (docs.sweep.dev).
+- [Getting Started](https://github.com/sweepai/sweep/blob/main/docs/pages/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Config](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/config.mdx)
+  Why it matters: authoritative reference on `Config` (github.com).
+- [Advanced Usage](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/advanced.mdx)
+  Why it matters: authoritative reference on `Advanced Usage` (github.com).
+- [CLI](https://github.com/sweepai/sweep/blob/main/docs/pages/cli.mdx)
+  Why it matters: authoritative reference on `CLI` (github.com).
+- [Deployment](https://github.com/sweepai/sweep/blob/main/docs/pages/deployment.mdx)
+  Why it matters: authoritative reference on `Deployment` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Sweep` and `participant` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started and Current Product Posture](01-getting-started-and-current-product-posture.md)
+- [Next Chapter: Chapter 3: Repository Configuration and Governance](03-repository-configuration-and-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/sweep-tutorial/03-repository-configuration-and-governance.md b/tutorials/sweep-tutorial/03-repository-configuration-and-governance.md
index 00775c41..fa582bc7 100644
--- a/tutorials/sweep-tutorial/03-repository-configuration-and-governance.md
+++ b/tutorials/sweep-tutorial/03-repository-configuration-and-governance.md
@@ -7,6 +7,9 @@ parent: Sweep Tutorial
 
 # Chapter 3: Repository Configuration and Governance
 
+Welcome to **Chapter 3: Repository Configuration and Governance**. In this part of **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on `sweep.yaml`, the main behavior contract for repository-level Sweep usage.
 
 ## Learning Goals
@@ -51,3 +54,599 @@ description: "Python 3.10 repo; follow PEP8 and update tests when modifying busi
 You now have a policy foundation for safer, more consistent Sweep behavior.
 
 Next: [Chapter 4: Feedback Loops, Review Comments, and CI Repair](04-feedback-loops-review-comments-and-ci-repair.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- tutorial slug: **sweep-tutorial**
+- chapter focus: **Chapter 3: Repository Configuration and Governance**
+- system context: **Sweep Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Repository Configuration and Governance`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Sweep Repository](https://github.com/sweepai/sweep)
+- [README](https://github.com/sweepai/sweep/blob/main/README.md)
+- [Docs Home](https://docs.sweep.dev/)
+- [Getting Started](https://github.com/sweepai/sweep/blob/main/docs/pages/getting-started.md)
+- [Config](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/config.mdx)
+- [Advanced Usage](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/advanced.mdx)
+- [CLI](https://github.com/sweepai/sweep/blob/main/docs/pages/cli.mdx)
+- [Deployment](https://github.com/sweepai/sweep/blob/main/docs/pages/deployment.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Tabby Tutorial](../tabby-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Stagewise Tutorial](../stagewise-tutorial/)
+- [Chapter 1: Getting Started and Current Product Posture](01-getting-started-and-current-product-posture.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Repository Configuration and Governance`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Repository Configuration and Governance
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `branch`, `main`, `gha_enabled` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Repository Configuration and Governance` as an operating subsystem inside **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `blocked_dirs`, `github`, `draft` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Repository Configuration and Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `branch`.
+2. **Input normalization**: shape incoming data so `main` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `gha_enabled`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Sweep Repository](https://github.com/sweepai/sweep)
+  Why it matters: authoritative reference on `Sweep Repository` (github.com).
+- [README](https://github.com/sweepai/sweep/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Docs Home](https://docs.sweep.dev/)
+  Why it matters: authoritative reference on `Docs Home` (docs.sweep.dev).
+- [Getting Started](https://github.com/sweepai/sweep/blob/main/docs/pages/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Config](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/config.mdx)
+  Why it matters: authoritative reference on `Config` (github.com).
+- [Advanced Usage](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/advanced.mdx)
+  Why it matters: authoritative reference on `Advanced Usage` (github.com).
+- [CLI](https://github.com/sweepai/sweep/blob/main/docs/pages/cli.mdx)
+  Why it matters: authoritative reference on `CLI` (github.com).
+- [Deployment](https://github.com/sweepai/sweep/blob/main/docs/pages/deployment.mdx)
+  Why it matters: authoritative reference on `Deployment` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `branch` and `main` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Issue to PR Workflow Architecture](02-issue-to-pr-workflow-architecture.md)
+- [Next Chapter: Chapter 4: Feedback Loops, Review Comments, and CI Repair](04-feedback-loops-review-comments-and-ci-repair.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/sweep-tutorial/04-feedback-loops-review-comments-and-ci-repair.md b/tutorials/sweep-tutorial/04-feedback-loops-review-comments-and-ci-repair.md
index f2fa20a8..8d7a167f 100644
--- a/tutorials/sweep-tutorial/04-feedback-loops-review-comments-and-ci-repair.md
+++ b/tutorials/sweep-tutorial/04-feedback-loops-review-comments-and-ci-repair.md
@@ -7,6 +7,9 @@ parent: Sweep Tutorial
 
 # Chapter 4: Feedback Loops, Review Comments, and CI Repair
 
+Welcome to **Chapter 4: Feedback Loops, Review Comments, and CI Repair**. In this part of **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Sweep outcomes improve when teams actively run comment-based feedback loops and CI repair cycles.
 
 ## Learning Goals
@@ -39,3 +42,607 @@ Sweep outcomes improve when teams actively run comment-based feedback loops and
 You now know how to turn generated PRs into high-quality merge candidates through structured feedback.
 
 Next: [Chapter 5: CLI and Self-Hosted Deployment](05-cli-and-self-hosted-deployment.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- tutorial slug: **sweep-tutorial**
+- chapter focus: **Chapter 4: Feedback Loops, Review Comments, and CI Repair**
+- system context: **Sweep Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Feedback Loops, Review Comments, and CI Repair`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Sweep Repository](https://github.com/sweepai/sweep)
+- [README](https://github.com/sweepai/sweep/blob/main/README.md)
+- [Docs Home](https://docs.sweep.dev/)
+- [Getting Started](https://github.com/sweepai/sweep/blob/main/docs/pages/getting-started.md)
+- [Config](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/config.mdx)
+- [Advanced Usage](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/advanced.mdx)
+- [CLI](https://github.com/sweepai/sweep/blob/main/docs/pages/cli.mdx)
+- [Deployment](https://github.com/sweepai/sweep/blob/main/docs/pages/deployment.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Tabby Tutorial](../tabby-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Stagewise Tutorial](../stagewise-tutorial/)
+- [Chapter 1: Getting Started and Current Product Posture](01-getting-started-and-current-product-posture.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Feedback Loops, Review Comments, and CI Repair`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Feedback Loops, Review Comments, and CI Repair
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Feedback Loops, Review Comments, and CI Repair` as an operating subsystem inside **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Feedback Loops, Review Comments, and CI Repair` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Sweep Repository](https://github.com/sweepai/sweep)
+  Why it matters: authoritative reference on `Sweep Repository` (github.com).
+- [README](https://github.com/sweepai/sweep/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Docs Home](https://docs.sweep.dev/)
+  Why it matters: authoritative reference on `Docs Home` (docs.sweep.dev).
+- [Getting Started](https://github.com/sweepai/sweep/blob/main/docs/pages/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Config](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/config.mdx)
+  Why it matters: authoritative reference on `Config` (github.com).
+- [Advanced Usage](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/advanced.mdx)
+  Why it matters: authoritative reference on `Advanced Usage` (github.com).
+- [CLI](https://github.com/sweepai/sweep/blob/main/docs/pages/cli.mdx)
+  Why it matters: authoritative reference on `CLI` (github.com).
+- [Deployment](https://github.com/sweepai/sweep/blob/main/docs/pages/deployment.mdx)
+  Why it matters: authoritative reference on `Deployment` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Repository Configuration and Governance](03-repository-configuration-and-governance.md)
+- [Next Chapter: Chapter 5: CLI and Self-Hosted Deployment](05-cli-and-self-hosted-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/sweep-tutorial/05-cli-and-self-hosted-deployment.md b/tutorials/sweep-tutorial/05-cli-and-self-hosted-deployment.md
index f7c2eda8..028805cc 100644
--- a/tutorials/sweep-tutorial/05-cli-and-self-hosted-deployment.md
+++ b/tutorials/sweep-tutorial/05-cli-and-self-hosted-deployment.md
@@ -7,6 +7,9 @@ parent: Sweep Tutorial
 
 # Chapter 5: CLI and Self-Hosted Deployment
 
+Welcome to **Chapter 5: CLI and Self-Hosted Deployment**. In this part of **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Sweep supports local CLI workflows and self-hosted GitHub app deployments for teams with tighter control requirements.
 
 ## Learning Goals
@@ -48,3 +51,599 @@ sweep run https://github.com/ORG/REPO/issues/1
 You now have a mode-selection model for operating Sweep in different risk and compliance contexts.
 
 Next: [Chapter 6: Search, Planning, and Execution Patterns](06-search-planning-and-execution-patterns.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- tutorial slug: **sweep-tutorial**
+- chapter focus: **Chapter 5: CLI and Self-Hosted Deployment**
+- system context: **Sweep Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: CLI and Self-Hosted Deployment`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Sweep Repository](https://github.com/sweepai/sweep)
+- [README](https://github.com/sweepai/sweep/blob/main/README.md)
+- [Docs Home](https://docs.sweep.dev/)
+- [Getting Started](https://github.com/sweepai/sweep/blob/main/docs/pages/getting-started.md)
+- [Config](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/config.mdx)
+- [Advanced Usage](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/advanced.mdx)
+- [CLI](https://github.com/sweepai/sweep/blob/main/docs/pages/cli.mdx)
+- [Deployment](https://github.com/sweepai/sweep/blob/main/docs/pages/deployment.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Tabby Tutorial](../tabby-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Stagewise Tutorial](../stagewise-tutorial/)
+- [Chapter 1: Getting Started and Current Product Posture](01-getting-started-and-current-product-posture.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: CLI and Self-Hosted Deployment`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: CLI and Self-Hosted Deployment
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `sweep`, `install`, `sweepai` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: CLI and Self-Hosted Deployment` as an operating subsystem inside **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `init`, `https`, `github` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: CLI and Self-Hosted Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `sweep`.
+2. **Input normalization**: shape incoming data so `install` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `sweepai`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Sweep Repository](https://github.com/sweepai/sweep)
+  Why it matters: authoritative reference on `Sweep Repository` (github.com).
+- [README](https://github.com/sweepai/sweep/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Docs Home](https://docs.sweep.dev/)
+  Why it matters: authoritative reference on `Docs Home` (docs.sweep.dev).
+- [Getting Started](https://github.com/sweepai/sweep/blob/main/docs/pages/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Config](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/config.mdx)
+  Why it matters: authoritative reference on `Config` (github.com).
+- [Advanced Usage](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/advanced.mdx)
+  Why it matters: authoritative reference on `Advanced Usage` (github.com).
+- [CLI](https://github.com/sweepai/sweep/blob/main/docs/pages/cli.mdx)
+  Why it matters: authoritative reference on `CLI` (github.com).
+- [Deployment](https://github.com/sweepai/sweep/blob/main/docs/pages/deployment.mdx)
+  Why it matters: authoritative reference on `Deployment` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `sweep` and `install` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Feedback Loops, Review Comments, and CI Repair](04-feedback-loops-review-comments-and-ci-repair.md)
+- [Next Chapter: Chapter 6: Search, Planning, and Execution Patterns](06-search-planning-and-execution-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/sweep-tutorial/06-search-planning-and-execution-patterns.md b/tutorials/sweep-tutorial/06-search-planning-and-execution-patterns.md
index 4af4cf6e..24385111 100644
--- a/tutorials/sweep-tutorial/06-search-planning-and-execution-patterns.md
+++ b/tutorials/sweep-tutorial/06-search-planning-and-execution-patterns.md
@@ -7,6 +7,9 @@ parent: Sweep Tutorial
 
 # Chapter 6: Search, Planning, and Execution Patterns
 
+Welcome to **Chapter 6: Search, Planning, and Execution Patterns**. In this part of **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Sweep performance depends on a consistent internal pattern: search, plan, implement, validate, and revise.
 
 ## Learning Goals
@@ -42,3 +45,595 @@ From project docs and FAQ, Sweep emphasizes a bounded workflow instead of open-d
 You now understand the core behavioral pattern that drives Sweep output quality.
 
 Next: [Chapter 7: Limitations, Risk Controls, and Safe Scope](07-limitations-risk-controls-and-safe-scope.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- tutorial slug: **sweep-tutorial**
+- chapter focus: **Chapter 6: Search, Planning, and Execution Patterns**
+- system context: **Sweep Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Search, Planning, and Execution Patterns`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Sweep Repository](https://github.com/sweepai/sweep)
+- [README](https://github.com/sweepai/sweep/blob/main/README.md)
+- [Docs Home](https://docs.sweep.dev/)
+- [Getting Started](https://github.com/sweepai/sweep/blob/main/docs/pages/getting-started.md)
+- [Config](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/config.mdx)
+- [Advanced Usage](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/advanced.mdx)
+- [CLI](https://github.com/sweepai/sweep/blob/main/docs/pages/cli.mdx)
+- [Deployment](https://github.com/sweepai/sweep/blob/main/docs/pages/deployment.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Tabby Tutorial](../tabby-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Stagewise Tutorial](../stagewise-tutorial/)
+- [Chapter 1: Getting Started and Current Product Posture](01-getting-started-and-current-product-posture.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Search, Planning, and Execution Patterns`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Search, Planning, and Execution Patterns
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Search, Planning, and Execution Patterns` as an operating subsystem inside **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Search, Planning, and Execution Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Sweep Repository](https://github.com/sweepai/sweep)
+  Why it matters: authoritative reference on `Sweep Repository` (github.com).
+- [README](https://github.com/sweepai/sweep/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Docs Home](https://docs.sweep.dev/)
+  Why it matters: authoritative reference on `Docs Home` (docs.sweep.dev).
+- [Getting Started](https://github.com/sweepai/sweep/blob/main/docs/pages/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Config](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/config.mdx)
+  Why it matters: authoritative reference on `Config` (github.com).
+- [Advanced Usage](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/advanced.mdx)
+  Why it matters: authoritative reference on `Advanced Usage` (github.com).
+- [CLI](https://github.com/sweepai/sweep/blob/main/docs/pages/cli.mdx)
+  Why it matters: authoritative reference on `CLI` (github.com).
+- [Deployment](https://github.com/sweepai/sweep/blob/main/docs/pages/deployment.mdx)
+  Why it matters: authoritative reference on `Deployment` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: CLI and Self-Hosted Deployment](05-cli-and-self-hosted-deployment.md)
+- [Next Chapter: Chapter 7: Limitations, Risk Controls, and Safe Scope](07-limitations-risk-controls-and-safe-scope.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/sweep-tutorial/07-limitations-risk-controls-and-safe-scope.md b/tutorials/sweep-tutorial/07-limitations-risk-controls-and-safe-scope.md
index 8371f9bb..ff76139d 100644
--- a/tutorials/sweep-tutorial/07-limitations-risk-controls-and-safe-scope.md
+++ b/tutorials/sweep-tutorial/07-limitations-risk-controls-and-safe-scope.md
@@ -7,6 +7,9 @@ parent: Sweep Tutorial
 
 # Chapter 7: Limitations, Risk Controls, and Safe Scope
 
+Welcome to **Chapter 7: Limitations, Risk Controls, and Safe Scope**. In this part of **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Sweep reliability is highly sensitive to task size and ambiguity. This chapter operationalizes safe scope boundaries.
 
 ## Learning Goals
@@ -39,3 +42,607 @@ Sweep reliability is highly sensitive to task size and ambiguity. This chapter o
 You now have a guardrail framework for assigning tasks Sweep can complete with high confidence.
 
 Next: [Chapter 8: Migration Strategy and Long-Term Operations](08-migration-strategy-and-long-term-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- tutorial slug: **sweep-tutorial**
+- chapter focus: **Chapter 7: Limitations, Risk Controls, and Safe Scope**
+- system context: **Sweep Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Limitations, Risk Controls, and Safe Scope`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Sweep Repository](https://github.com/sweepai/sweep)
+- [README](https://github.com/sweepai/sweep/blob/main/README.md)
+- [Docs Home](https://docs.sweep.dev/)
+- [Getting Started](https://github.com/sweepai/sweep/blob/main/docs/pages/getting-started.md)
+- [Config](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/config.mdx)
+- [Advanced Usage](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/advanced.mdx)
+- [CLI](https://github.com/sweepai/sweep/blob/main/docs/pages/cli.mdx)
+- [Deployment](https://github.com/sweepai/sweep/blob/main/docs/pages/deployment.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Tabby Tutorial](../tabby-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Stagewise Tutorial](../stagewise-tutorial/)
+- [Chapter 1: Getting Started and Current Product Posture](01-getting-started-and-current-product-posture.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Limitations, Risk Controls, and Safe Scope`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Limitations, Risk Controls, and Safe Scope
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Limitations, Risk Controls, and Safe Scope` as an operating subsystem inside **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Limitations, Risk Controls, and Safe Scope` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Sweep Repository](https://github.com/sweepai/sweep)
+  Why it matters: authoritative reference on `Sweep Repository` (github.com).
+- [README](https://github.com/sweepai/sweep/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Docs Home](https://docs.sweep.dev/)
+  Why it matters: authoritative reference on `Docs Home` (docs.sweep.dev).
+- [Getting Started](https://github.com/sweepai/sweep/blob/main/docs/pages/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Config](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/config.mdx)
+  Why it matters: authoritative reference on `Config` (github.com).
+- [Advanced Usage](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/advanced.mdx)
+  Why it matters: authoritative reference on `Advanced Usage` (github.com).
+- [CLI](https://github.com/sweepai/sweep/blob/main/docs/pages/cli.mdx)
+  Why it matters: authoritative reference on `CLI` (github.com).
+- [Deployment](https://github.com/sweepai/sweep/blob/main/docs/pages/deployment.mdx)
+  Why it matters: authoritative reference on `Deployment` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Search, Planning, and Execution Patterns](06-search-planning-and-execution-patterns.md)
+- [Next Chapter: Chapter 8: Migration Strategy and Long-Term Operations](08-migration-strategy-and-long-term-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/sweep-tutorial/08-migration-strategy-and-long-term-operations.md b/tutorials/sweep-tutorial/08-migration-strategy-and-long-term-operations.md
index 2b342465..0c38cc85 100644
--- a/tutorials/sweep-tutorial/08-migration-strategy-and-long-term-operations.md
+++ b/tutorials/sweep-tutorial/08-migration-strategy-and-long-term-operations.md
@@ -7,6 +7,9 @@ parent: Sweep Tutorial
 
 # Chapter 8: Migration Strategy and Long-Term Operations
 
+Welcome to **Chapter 8: Migration Strategy and Long-Term Operations**. In this part of **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 The Sweep ecosystem has evolved over time. Teams need an explicit strategy to preserve value while adapting tooling.
 
 ## Learning Goals
@@ -40,3 +43,606 @@ The Sweep ecosystem has evolved over time. Teams need an explicit strategy to pr
 You now have a long-term operating approach for using Sweep responsibly within a changing coding-agent landscape.
 
 Next: compare adjacent architectures in [OpenCode](../opencode-tutorial/) and [Stagewise](../stagewise-tutorial/).
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- tutorial slug: **sweep-tutorial**
+- chapter focus: **Chapter 8: Migration Strategy and Long-Term Operations**
+- system context: **Sweep Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Migration Strategy and Long-Term Operations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Sweep Repository](https://github.com/sweepai/sweep)
+- [README](https://github.com/sweepai/sweep/blob/main/README.md)
+- [Docs Home](https://docs.sweep.dev/)
+- [Getting Started](https://github.com/sweepai/sweep/blob/main/docs/pages/getting-started.md)
+- [Config](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/config.mdx)
+- [Advanced Usage](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/advanced.mdx)
+- [CLI](https://github.com/sweepai/sweep/blob/main/docs/pages/cli.mdx)
+- [Deployment](https://github.com/sweepai/sweep/blob/main/docs/pages/deployment.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Tabby Tutorial](../tabby-tutorial/)
+- [Continue Tutorial](../continue-tutorial/)
+- [Stagewise Tutorial](../stagewise-tutorial/)
+- [Chapter 1: Getting Started and Current Product Posture](01-getting-started-and-current-product-posture.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Migration Strategy and Long-Term Operations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Migration Strategy and Long-Term Operations
+
+- tutorial context: **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Migration Strategy and Long-Term Operations` as an operating subsystem inside **Sweep Tutorial: Issue-to-PR AI Coding Workflows on GitHub**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Migration Strategy and Long-Term Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Sweep Repository](https://github.com/sweepai/sweep)
+  Why it matters: authoritative reference on `Sweep Repository` (github.com).
+- [README](https://github.com/sweepai/sweep/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Docs Home](https://docs.sweep.dev/)
+  Why it matters: authoritative reference on `Docs Home` (docs.sweep.dev).
+- [Getting Started](https://github.com/sweepai/sweep/blob/main/docs/pages/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Config](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/config.mdx)
+  Why it matters: authoritative reference on `Config` (github.com).
+- [Advanced Usage](https://github.com/sweepai/sweep/blob/main/docs/pages/usage/advanced.mdx)
+  Why it matters: authoritative reference on `Advanced Usage` (github.com).
+- [CLI](https://github.com/sweepai/sweep/blob/main/docs/pages/cli.mdx)
+  Why it matters: authoritative reference on `CLI` (github.com).
+- [Deployment](https://github.com/sweepai/sweep/blob/main/docs/pages/deployment.mdx)
+  Why it matters: authoritative reference on `Deployment` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Limitations, Risk Controls, and Safe Scope](07-limitations-risk-controls-and-safe-scope.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/tabby-tutorial/01-getting-started-and-first-server.md b/tutorials/tabby-tutorial/01-getting-started-and-first-server.md
index 0dc28330..021112b1 100644
--- a/tutorials/tabby-tutorial/01-getting-started-and-first-server.md
+++ b/tutorials/tabby-tutorial/01-getting-started-and-first-server.md
@@ -7,6 +7,9 @@ parent: Tabby Tutorial
 
 # Chapter 1: Getting Started and First Server
 
+Welcome to **Chapter 1: Getting Started and First Server**. In this part of **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Tabby running with a clean local baseline so every later chapter can focus on architecture and operations instead of setup drift.
 
 ## Learning Goals
@@ -68,3 +71,574 @@ Then open `http://localhost:8080` and complete account registration.
 You now have a working Tabby deployment with at least one connected editor client.
 
 Next: [Chapter 2: Architecture and Runtime Components](02-architecture-and-runtime-components.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- tutorial slug: **tabby-tutorial**
+- chapter focus: **Chapter 1: Getting Started and First Server**
+- system context: **Tabby Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started and First Server`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Tabby Repository](https://github.com/TabbyML/tabby)
+- [Tabby README](https://github.com/TabbyML/tabby/blob/main/README.md)
+- [Welcome Docs](https://tabby.tabbyml.com/docs/welcome/)
+- [Docker Installation](https://tabby.tabbyml.com/docs/quick-start/installation/docker)
+- [Connect IDE Extensions](https://tabby.tabbyml.com/docs/quick-start/setup-ide)
+- [Config TOML](https://tabby.tabbyml.com/docs/administration/config-toml)
+- [Upgrade Guide](https://tabby.tabbyml.com/docs/administration/upgrade)
+- [tabby-agent README](https://github.com/TabbyML/tabby/blob/main/clients/tabby-agent/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Continue Tutorial](../continue-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started and First Server](01-getting-started-and-first-server.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started and First Server`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started and First Server
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `tabby`, `tabbyml`, `model` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started and First Server` as an operating subsystem inside **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `docker`, `name`, `gpus` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started and First Server` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `tabby`.
+2. **Input normalization**: shape incoming data so `tabbyml` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Tabby Repository](https://github.com/TabbyML/tabby)
+  Why it matters: authoritative reference on `Tabby Repository` (github.com).
+- [Tabby README](https://github.com/TabbyML/tabby/blob/main/README.md)
+  Why it matters: authoritative reference on `Tabby README` (github.com).
+- [Welcome Docs](https://tabby.tabbyml.com/docs/welcome/)
+  Why it matters: authoritative reference on `Welcome Docs` (tabby.tabbyml.com).
+- [Docker Installation](https://tabby.tabbyml.com/docs/quick-start/installation/docker)
+  Why it matters: authoritative reference on `Docker Installation` (tabby.tabbyml.com).
+- [Connect IDE Extensions](https://tabby.tabbyml.com/docs/quick-start/setup-ide)
+  Why it matters: authoritative reference on `Connect IDE Extensions` (tabby.tabbyml.com).
+- [Config TOML](https://tabby.tabbyml.com/docs/administration/config-toml)
+  Why it matters: authoritative reference on `Config TOML` (tabby.tabbyml.com).
+- [Upgrade Guide](https://tabby.tabbyml.com/docs/administration/upgrade)
+  Why it matters: authoritative reference on `Upgrade Guide` (tabby.tabbyml.com).
+- [tabby-agent README](https://github.com/TabbyML/tabby/blob/main/clients/tabby-agent/README.md)
+  Why it matters: authoritative reference on `tabby-agent README` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `tabby` and `tabbyml` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Architecture and Runtime Components](02-architecture-and-runtime-components.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/tabby-tutorial/02-architecture-and-runtime-components.md b/tutorials/tabby-tutorial/02-architecture-and-runtime-components.md
index 6af55c6e..acf81ebf 100644
--- a/tutorials/tabby-tutorial/02-architecture-and-runtime-components.md
+++ b/tutorials/tabby-tutorial/02-architecture-and-runtime-components.md
@@ -7,6 +7,9 @@ parent: Tabby Tutorial
 
 # Chapter 2: Architecture and Runtime Components
 
+Welcome to **Chapter 2: Architecture and Runtime Components**. In this part of **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Tabby is more than a single completion endpoint. It is a layered runtime that combines server services, context processing, and editor-facing agent bridges.
 
 ## Learning Goals
@@ -71,3 +74,575 @@ sequenceDiagram
 You now have a structural map for where behavior lives and how requests move across Tabby.
 
 Next: [Chapter 3: Model Serving and Completion Pipeline](03-model-serving-and-completion-pipeline.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- tutorial slug: **tabby-tutorial**
+- chapter focus: **Chapter 2: Architecture and Runtime Components**
+- system context: **Tabby Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Architecture and Runtime Components`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Tabby Repository](https://github.com/TabbyML/tabby)
+- [Tabby README](https://github.com/TabbyML/tabby/blob/main/README.md)
+- [Welcome Docs](https://tabby.tabbyml.com/docs/welcome/)
+- [Docker Installation](https://tabby.tabbyml.com/docs/quick-start/installation/docker)
+- [Connect IDE Extensions](https://tabby.tabbyml.com/docs/quick-start/setup-ide)
+- [Config TOML](https://tabby.tabbyml.com/docs/administration/config-toml)
+- [Upgrade Guide](https://tabby.tabbyml.com/docs/administration/upgrade)
+- [tabby-agent README](https://github.com/TabbyML/tabby/blob/main/clients/tabby-agent/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Continue Tutorial](../continue-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started and First Server](01-getting-started-and-first-server.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Architecture and Runtime Components`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Architecture and Runtime Components
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `participant`, `Agent`, `Model` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Architecture and Runtime Components` as an operating subsystem inside **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Tabby`, `completion`, `chat` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Architecture and Runtime Components` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `participant`.
+2. **Input normalization**: shape incoming data so `Agent` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Tabby Repository](https://github.com/TabbyML/tabby)
+  Why it matters: authoritative reference on `Tabby Repository` (github.com).
+- [Tabby README](https://github.com/TabbyML/tabby/blob/main/README.md)
+  Why it matters: authoritative reference on `Tabby README` (github.com).
+- [Welcome Docs](https://tabby.tabbyml.com/docs/welcome/)
+  Why it matters: authoritative reference on `Welcome Docs` (tabby.tabbyml.com).
+- [Docker Installation](https://tabby.tabbyml.com/docs/quick-start/installation/docker)
+  Why it matters: authoritative reference on `Docker Installation` (tabby.tabbyml.com).
+- [Connect IDE Extensions](https://tabby.tabbyml.com/docs/quick-start/setup-ide)
+  Why it matters: authoritative reference on `Connect IDE Extensions` (tabby.tabbyml.com).
+- [Config TOML](https://tabby.tabbyml.com/docs/administration/config-toml)
+  Why it matters: authoritative reference on `Config TOML` (tabby.tabbyml.com).
+- [Upgrade Guide](https://tabby.tabbyml.com/docs/administration/upgrade)
+  Why it matters: authoritative reference on `Upgrade Guide` (tabby.tabbyml.com).
+- [tabby-agent README](https://github.com/TabbyML/tabby/blob/main/clients/tabby-agent/README.md)
+  Why it matters: authoritative reference on `tabby-agent README` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `participant` and `Agent` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started and First Server](01-getting-started-and-first-server.md)
+- [Next Chapter: Chapter 3: Model Serving and Completion Pipeline](03-model-serving-and-completion-pipeline.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/tabby-tutorial/03-model-serving-and-completion-pipeline.md b/tutorials/tabby-tutorial/03-model-serving-and-completion-pipeline.md
index 09d0e80b..86f58ee3 100644
--- a/tutorials/tabby-tutorial/03-model-serving-and-completion-pipeline.md
+++ b/tutorials/tabby-tutorial/03-model-serving-and-completion-pipeline.md
@@ -7,6 +7,9 @@ parent: Tabby Tutorial
 
 # Chapter 3: Model Serving and Completion Pipeline
 
+Welcome to **Chapter 3: Model Serving and Completion Pipeline**. In this part of **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on how Tabby combines completion, chat, and embedding configuration into practical response quality.
 
 ## Learning Goals
@@ -67,3 +70,575 @@ Use a completion-capable model path that matches your deployment target (local m
 You now understand how model role separation drives both quality and operational cost.
 
 Next: [Chapter 4: Answer Engine and Context Indexing](04-answer-engine-and-context-indexing.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- tutorial slug: **tabby-tutorial**
+- chapter focus: **Chapter 3: Model Serving and Completion Pipeline**
+- system context: **Tabby Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Model Serving and Completion Pipeline`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Tabby Repository](https://github.com/TabbyML/tabby)
+- [Tabby README](https://github.com/TabbyML/tabby/blob/main/README.md)
+- [Welcome Docs](https://tabby.tabbyml.com/docs/welcome/)
+- [Docker Installation](https://tabby.tabbyml.com/docs/quick-start/installation/docker)
+- [Connect IDE Extensions](https://tabby.tabbyml.com/docs/quick-start/setup-ide)
+- [Config TOML](https://tabby.tabbyml.com/docs/administration/config-toml)
+- [Upgrade Guide](https://tabby.tabbyml.com/docs/administration/upgrade)
+- [tabby-agent README](https://github.com/TabbyML/tabby/blob/main/clients/tabby-agent/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Continue Tutorial](../continue-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started and First Server](01-getting-started-and-first-server.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Model Serving and Completion Pipeline`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Model Serving and Completion Pipeline
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `openai`, `embedding`, `model` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Model Serving and Completion Pipeline` as an operating subsystem inside **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `chat`, `http`, `kind` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Model Serving and Completion Pipeline` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `openai`.
+2. **Input normalization**: shape incoming data so `embedding` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Tabby Repository](https://github.com/TabbyML/tabby)
+  Why it matters: authoritative reference on `Tabby Repository` (github.com).
+- [Tabby README](https://github.com/TabbyML/tabby/blob/main/README.md)
+  Why it matters: authoritative reference on `Tabby README` (github.com).
+- [Welcome Docs](https://tabby.tabbyml.com/docs/welcome/)
+  Why it matters: authoritative reference on `Welcome Docs` (tabby.tabbyml.com).
+- [Docker Installation](https://tabby.tabbyml.com/docs/quick-start/installation/docker)
+  Why it matters: authoritative reference on `Docker Installation` (tabby.tabbyml.com).
+- [Connect IDE Extensions](https://tabby.tabbyml.com/docs/quick-start/setup-ide)
+  Why it matters: authoritative reference on `Connect IDE Extensions` (tabby.tabbyml.com).
+- [Config TOML](https://tabby.tabbyml.com/docs/administration/config-toml)
+  Why it matters: authoritative reference on `Config TOML` (tabby.tabbyml.com).
+- [Upgrade Guide](https://tabby.tabbyml.com/docs/administration/upgrade)
+  Why it matters: authoritative reference on `Upgrade Guide` (tabby.tabbyml.com).
+- [tabby-agent README](https://github.com/TabbyML/tabby/blob/main/clients/tabby-agent/README.md)
+  Why it matters: authoritative reference on `tabby-agent README` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `openai` and `embedding` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Architecture and Runtime Components](02-architecture-and-runtime-components.md)
+- [Next Chapter: Chapter 4: Answer Engine and Context Indexing](04-answer-engine-and-context-indexing.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/tabby-tutorial/04-answer-engine-and-context-indexing.md b/tutorials/tabby-tutorial/04-answer-engine-and-context-indexing.md
index 66382b6d..503dacb3 100644
--- a/tutorials/tabby-tutorial/04-answer-engine-and-context-indexing.md
+++ b/tutorials/tabby-tutorial/04-answer-engine-and-context-indexing.md
@@ -7,6 +7,9 @@ parent: Tabby Tutorial
 
 # Chapter 4: Answer Engine and Context Indexing
 
+Welcome to **Chapter 4: Answer Engine and Context Indexing**. In this part of **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Tabby quality depends on context. This chapter covers how indexing and answer workflows convert repository state into grounded responses.
 
 ## Learning Goals
@@ -56,3 +59,587 @@ The changelog documents ongoing work around context quality, including custom do
 You now have a practical model for operating Tabby as a context-grounded assistant instead of a bare autocomplete endpoint.
 
 Next: [Chapter 5: Editor Agents and Client Integrations](05-editor-agents-and-client-integrations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- tutorial slug: **tabby-tutorial**
+- chapter focus: **Chapter 4: Answer Engine and Context Indexing**
+- system context: **Tabby Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Answer Engine and Context Indexing`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Tabby Repository](https://github.com/TabbyML/tabby)
+- [Tabby README](https://github.com/TabbyML/tabby/blob/main/README.md)
+- [Welcome Docs](https://tabby.tabbyml.com/docs/welcome/)
+- [Docker Installation](https://tabby.tabbyml.com/docs/quick-start/installation/docker)
+- [Connect IDE Extensions](https://tabby.tabbyml.com/docs/quick-start/setup-ide)
+- [Config TOML](https://tabby.tabbyml.com/docs/administration/config-toml)
+- [Upgrade Guide](https://tabby.tabbyml.com/docs/administration/upgrade)
+- [tabby-agent README](https://github.com/TabbyML/tabby/blob/main/clients/tabby-agent/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Continue Tutorial](../continue-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started and First Server](01-getting-started-and-first-server.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Answer Engine and Context Indexing`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Answer Engine and Context Indexing
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `flowchart`, `Repository`, `docs` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Answer Engine and Context Indexing` as an operating subsystem inside **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `sources`, `Indexing`, `jobs` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Answer Engine and Context Indexing` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `flowchart`.
+2. **Input normalization**: shape incoming data so `Repository` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `docs`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Tabby Repository](https://github.com/TabbyML/tabby)
+  Why it matters: authoritative reference on `Tabby Repository` (github.com).
+- [Tabby README](https://github.com/TabbyML/tabby/blob/main/README.md)
+  Why it matters: authoritative reference on `Tabby README` (github.com).
+- [Welcome Docs](https://tabby.tabbyml.com/docs/welcome/)
+  Why it matters: authoritative reference on `Welcome Docs` (tabby.tabbyml.com).
+- [Docker Installation](https://tabby.tabbyml.com/docs/quick-start/installation/docker)
+  Why it matters: authoritative reference on `Docker Installation` (tabby.tabbyml.com).
+- [Connect IDE Extensions](https://tabby.tabbyml.com/docs/quick-start/setup-ide)
+  Why it matters: authoritative reference on `Connect IDE Extensions` (tabby.tabbyml.com).
+- [Config TOML](https://tabby.tabbyml.com/docs/administration/config-toml)
+  Why it matters: authoritative reference on `Config TOML` (tabby.tabbyml.com).
+- [Upgrade Guide](https://tabby.tabbyml.com/docs/administration/upgrade)
+  Why it matters: authoritative reference on `Upgrade Guide` (tabby.tabbyml.com).
+- [tabby-agent README](https://github.com/TabbyML/tabby/blob/main/clients/tabby-agent/README.md)
+  Why it matters: authoritative reference on `tabby-agent README` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `flowchart` and `Repository` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Model Serving and Completion Pipeline](03-model-serving-and-completion-pipeline.md)
+- [Next Chapter: Chapter 5: Editor Agents and Client Integrations](05-editor-agents-and-client-integrations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/tabby-tutorial/05-editor-agents-and-client-integrations.md b/tutorials/tabby-tutorial/05-editor-agents-and-client-integrations.md
index 0fd9b101..8dcae7d9 100644
--- a/tutorials/tabby-tutorial/05-editor-agents-and-client-integrations.md
+++ b/tutorials/tabby-tutorial/05-editor-agents-and-client-integrations.md
@@ -7,6 +7,9 @@ parent: Tabby Tutorial
 
 # Chapter 5: Editor Agents and Client Integrations
 
+Welcome to **Chapter 5: Editor Agents and Client Integrations**. In this part of **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on the client side: extension behavior, `tabby-agent`, and custom editor wiring.
 
 ## Learning Goals
@@ -56,3 +59,587 @@ args = ["tabby-agent", "--stdio"]
 You now know how to integrate Tabby clients beyond default setup paths and keep editor behavior predictable.
 
 Next: [Chapter 6: Configuration, Security, and Enterprise Controls](06-configuration-security-and-enterprise-controls.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- tutorial slug: **tabby-tutorial**
+- chapter focus: **Chapter 5: Editor Agents and Client Integrations**
+- system context: **Tabby Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Editor Agents and Client Integrations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Tabby Repository](https://github.com/TabbyML/tabby)
+- [Tabby README](https://github.com/TabbyML/tabby/blob/main/README.md)
+- [Welcome Docs](https://tabby.tabbyml.com/docs/welcome/)
+- [Docker Installation](https://tabby.tabbyml.com/docs/quick-start/installation/docker)
+- [Connect IDE Extensions](https://tabby.tabbyml.com/docs/quick-start/setup-ide)
+- [Config TOML](https://tabby.tabbyml.com/docs/administration/config-toml)
+- [Upgrade Guide](https://tabby.tabbyml.com/docs/administration/upgrade)
+- [tabby-agent README](https://github.com/TabbyML/tabby/blob/main/clients/tabby-agent/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Continue Tutorial](../continue-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started and First Server](01-getting-started-and-first-server.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Editor Agents and Client Integrations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Editor Agents and Client Integrations
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `tabby`, `agent`, `stdio` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Editor Agents and Client Integrations` as an operating subsystem inside **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `language`, `server`, `command` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Editor Agents and Client Integrations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `tabby`.
+2. **Input normalization**: shape incoming data so `agent` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `stdio`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Tabby Repository](https://github.com/TabbyML/tabby)
+  Why it matters: authoritative reference on `Tabby Repository` (github.com).
+- [Tabby README](https://github.com/TabbyML/tabby/blob/main/README.md)
+  Why it matters: authoritative reference on `Tabby README` (github.com).
+- [Welcome Docs](https://tabby.tabbyml.com/docs/welcome/)
+  Why it matters: authoritative reference on `Welcome Docs` (tabby.tabbyml.com).
+- [Docker Installation](https://tabby.tabbyml.com/docs/quick-start/installation/docker)
+  Why it matters: authoritative reference on `Docker Installation` (tabby.tabbyml.com).
+- [Connect IDE Extensions](https://tabby.tabbyml.com/docs/quick-start/setup-ide)
+  Why it matters: authoritative reference on `Connect IDE Extensions` (tabby.tabbyml.com).
+- [Config TOML](https://tabby.tabbyml.com/docs/administration/config-toml)
+  Why it matters: authoritative reference on `Config TOML` (tabby.tabbyml.com).
+- [Upgrade Guide](https://tabby.tabbyml.com/docs/administration/upgrade)
+  Why it matters: authoritative reference on `Upgrade Guide` (tabby.tabbyml.com).
+- [tabby-agent README](https://github.com/TabbyML/tabby/blob/main/clients/tabby-agent/README.md)
+  Why it matters: authoritative reference on `tabby-agent README` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `tabby` and `agent` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Answer Engine and Context Indexing](04-answer-engine-and-context-indexing.md)
+- [Next Chapter: Chapter 6: Configuration, Security, and Enterprise Controls](06-configuration-security-and-enterprise-controls.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/tabby-tutorial/06-configuration-security-and-enterprise-controls.md b/tutorials/tabby-tutorial/06-configuration-security-and-enterprise-controls.md
index d116546f..0bfa7c80 100644
--- a/tutorials/tabby-tutorial/06-configuration-security-and-enterprise-controls.md
+++ b/tutorials/tabby-tutorial/06-configuration-security-and-enterprise-controls.md
@@ -7,6 +7,9 @@ parent: Tabby Tutorial
 
 # Chapter 6: Configuration, Security, and Enterprise Controls
 
+Welcome to **Chapter 6: Configuration, Security, and Enterprise Controls**. In this part of **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 As Tabby moves from single-user setup to team deployment, security and policy controls become central.
 
 ## Learning Goals
@@ -59,3 +62,587 @@ Prefer codebase-grounded answers and explicit uncertainty.
 You now have a concrete security checklist for moving Tabby into shared environments.
 
 Next: [Chapter 7: Operations, Upgrades, and Observability](07-operations-upgrades-and-observability.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- tutorial slug: **tabby-tutorial**
+- chapter focus: **Chapter 6: Configuration, Security, and Enterprise Controls**
+- system context: **Tabby Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Configuration, Security, and Enterprise Controls`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Tabby Repository](https://github.com/TabbyML/tabby)
+- [Tabby README](https://github.com/TabbyML/tabby/blob/main/README.md)
+- [Welcome Docs](https://tabby.tabbyml.com/docs/welcome/)
+- [Docker Installation](https://tabby.tabbyml.com/docs/quick-start/installation/docker)
+- [Connect IDE Extensions](https://tabby.tabbyml.com/docs/quick-start/setup-ide)
+- [Config TOML](https://tabby.tabbyml.com/docs/administration/config-toml)
+- [Upgrade Guide](https://tabby.tabbyml.com/docs/administration/upgrade)
+- [tabby-agent README](https://github.com/TabbyML/tabby/blob/main/clients/tabby-agent/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Continue Tutorial](../continue-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started and First Server](01-getting-started-and-first-server.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Configuration, Security, and Enterprise Controls`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Configuration, Security, and Enterprise Controls
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `answer`, `system_prompt`, `Tabby` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Configuration, Security, and Enterprise Controls` as an operating subsystem inside **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `internal`, `engineering`, `support` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Configuration, Security, and Enterprise Controls` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `answer`.
+2. **Input normalization**: shape incoming data so `system_prompt` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Tabby`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Tabby Repository](https://github.com/TabbyML/tabby)
+  Why it matters: authoritative reference on `Tabby Repository` (github.com).
+- [Tabby README](https://github.com/TabbyML/tabby/blob/main/README.md)
+  Why it matters: authoritative reference on `Tabby README` (github.com).
+- [Welcome Docs](https://tabby.tabbyml.com/docs/welcome/)
+  Why it matters: authoritative reference on `Welcome Docs` (tabby.tabbyml.com).
+- [Docker Installation](https://tabby.tabbyml.com/docs/quick-start/installation/docker)
+  Why it matters: authoritative reference on `Docker Installation` (tabby.tabbyml.com).
+- [Connect IDE Extensions](https://tabby.tabbyml.com/docs/quick-start/setup-ide)
+  Why it matters: authoritative reference on `Connect IDE Extensions` (tabby.tabbyml.com).
+- [Config TOML](https://tabby.tabbyml.com/docs/administration/config-toml)
+  Why it matters: authoritative reference on `Config TOML` (tabby.tabbyml.com).
+- [Upgrade Guide](https://tabby.tabbyml.com/docs/administration/upgrade)
+  Why it matters: authoritative reference on `Upgrade Guide` (tabby.tabbyml.com).
+- [tabby-agent README](https://github.com/TabbyML/tabby/blob/main/clients/tabby-agent/README.md)
+  Why it matters: authoritative reference on `tabby-agent README` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `answer` and `system_prompt` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Editor Agents and Client Integrations](05-editor-agents-and-client-integrations.md)
+- [Next Chapter: Chapter 7: Operations, Upgrades, and Observability](07-operations-upgrades-and-observability.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/tabby-tutorial/07-operations-upgrades-and-observability.md b/tutorials/tabby-tutorial/07-operations-upgrades-and-observability.md
index a22dda75..5bc423e9 100644
--- a/tutorials/tabby-tutorial/07-operations-upgrades-and-observability.md
+++ b/tutorials/tabby-tutorial/07-operations-upgrades-and-observability.md
@@ -7,6 +7,9 @@ parent: Tabby Tutorial
 
 # Chapter 7: Operations, Upgrades, and Observability
 
+Welcome to **Chapter 7: Operations, Upgrades, and Observability**. In this part of **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Long-term reliability comes from disciplined upgrades, backup paths, and visibility into failures.
 
 ## Learning Goals
@@ -49,3 +52,595 @@ Long-term reliability comes from disciplined upgrades, backup paths, and visibil
 You now have a practical operations frame for safely evolving Tabby over time.
 
 Next: [Chapter 8: Contribution, Roadmap, and Team Adoption](08-contribution-roadmap-and-team-adoption.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- tutorial slug: **tabby-tutorial**
+- chapter focus: **Chapter 7: Operations, Upgrades, and Observability**
+- system context: **Tabby Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Operations, Upgrades, and Observability`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Tabby Repository](https://github.com/TabbyML/tabby)
+- [Tabby README](https://github.com/TabbyML/tabby/blob/main/README.md)
+- [Welcome Docs](https://tabby.tabbyml.com/docs/welcome/)
+- [Docker Installation](https://tabby.tabbyml.com/docs/quick-start/installation/docker)
+- [Connect IDE Extensions](https://tabby.tabbyml.com/docs/quick-start/setup-ide)
+- [Config TOML](https://tabby.tabbyml.com/docs/administration/config-toml)
+- [Upgrade Guide](https://tabby.tabbyml.com/docs/administration/upgrade)
+- [tabby-agent README](https://github.com/TabbyML/tabby/blob/main/clients/tabby-agent/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Continue Tutorial](../continue-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started and First Server](01-getting-started-and-first-server.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Operations, Upgrades, and Observability`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Operations, Upgrades, and Observability
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Operations, Upgrades, and Observability` as an operating subsystem inside **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Operations, Upgrades, and Observability` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Tabby Repository](https://github.com/TabbyML/tabby)
+  Why it matters: authoritative reference on `Tabby Repository` (github.com).
+- [Tabby README](https://github.com/TabbyML/tabby/blob/main/README.md)
+  Why it matters: authoritative reference on `Tabby README` (github.com).
+- [Welcome Docs](https://tabby.tabbyml.com/docs/welcome/)
+  Why it matters: authoritative reference on `Welcome Docs` (tabby.tabbyml.com).
+- [Docker Installation](https://tabby.tabbyml.com/docs/quick-start/installation/docker)
+  Why it matters: authoritative reference on `Docker Installation` (tabby.tabbyml.com).
+- [Connect IDE Extensions](https://tabby.tabbyml.com/docs/quick-start/setup-ide)
+  Why it matters: authoritative reference on `Connect IDE Extensions` (tabby.tabbyml.com).
+- [Config TOML](https://tabby.tabbyml.com/docs/administration/config-toml)
+  Why it matters: authoritative reference on `Config TOML` (tabby.tabbyml.com).
+- [Upgrade Guide](https://tabby.tabbyml.com/docs/administration/upgrade)
+  Why it matters: authoritative reference on `Upgrade Guide` (tabby.tabbyml.com).
+- [tabby-agent README](https://github.com/TabbyML/tabby/blob/main/clients/tabby-agent/README.md)
+  Why it matters: authoritative reference on `tabby-agent README` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Configuration, Security, and Enterprise Controls](06-configuration-security-and-enterprise-controls.md)
+- [Next Chapter: Chapter 8: Contribution, Roadmap, and Team Adoption](08-contribution-roadmap-and-team-adoption.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/tabby-tutorial/08-contribution-roadmap-and-team-adoption.md b/tutorials/tabby-tutorial/08-contribution-roadmap-and-team-adoption.md
index 040a22dd..42d0ba2e 100644
--- a/tutorials/tabby-tutorial/08-contribution-roadmap-and-team-adoption.md
+++ b/tutorials/tabby-tutorial/08-contribution-roadmap-and-team-adoption.md
@@ -7,6 +7,9 @@ parent: Tabby Tutorial
 
 # Chapter 8: Contribution, Roadmap, and Team Adoption
 
+Welcome to **Chapter 8: Contribution, Roadmap, and Team Adoption**. In this part of **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter closes the track with contribution mechanics and rollout strategy for engineering organizations.
 
 ## Learning Goals
@@ -47,3 +50,594 @@ This chapter closes the track with contribution mechanics and rollout strategy f
 You now have a full lifecycle mental model for adopting, operating, and extending Tabby as an internal coding assistant platform.
 
 Next: pick a related implementation track such as [Continue](../continue-tutorial/) or [OpenCode](../opencode-tutorial/).
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- tutorial slug: **tabby-tutorial**
+- chapter focus: **Chapter 8: Contribution, Roadmap, and Team Adoption**
+- system context: **Tabby Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Contribution, Roadmap, and Team Adoption`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Tabby Repository](https://github.com/TabbyML/tabby)
+- [Tabby README](https://github.com/TabbyML/tabby/blob/main/README.md)
+- [Welcome Docs](https://tabby.tabbyml.com/docs/welcome/)
+- [Docker Installation](https://tabby.tabbyml.com/docs/quick-start/installation/docker)
+- [Connect IDE Extensions](https://tabby.tabbyml.com/docs/quick-start/setup-ide)
+- [Config TOML](https://tabby.tabbyml.com/docs/administration/config-toml)
+- [Upgrade Guide](https://tabby.tabbyml.com/docs/administration/upgrade)
+- [tabby-agent README](https://github.com/TabbyML/tabby/blob/main/clients/tabby-agent/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Continue Tutorial](../continue-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started and First Server](01-getting-started-and-first-server.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Contribution, Roadmap, and Team Adoption`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Contribution, Roadmap, and Team Adoption
+
+- tutorial context: **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Contribution, Roadmap, and Team Adoption` as an operating subsystem inside **Tabby Tutorial: Self-Hosted AI Coding Assistant Architecture and Operations**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Contribution, Roadmap, and Team Adoption` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Tabby Repository](https://github.com/TabbyML/tabby)
+  Why it matters: authoritative reference on `Tabby Repository` (github.com).
+- [Tabby README](https://github.com/TabbyML/tabby/blob/main/README.md)
+  Why it matters: authoritative reference on `Tabby README` (github.com).
+- [Welcome Docs](https://tabby.tabbyml.com/docs/welcome/)
+  Why it matters: authoritative reference on `Welcome Docs` (tabby.tabbyml.com).
+- [Docker Installation](https://tabby.tabbyml.com/docs/quick-start/installation/docker)
+  Why it matters: authoritative reference on `Docker Installation` (tabby.tabbyml.com).
+- [Connect IDE Extensions](https://tabby.tabbyml.com/docs/quick-start/setup-ide)
+  Why it matters: authoritative reference on `Connect IDE Extensions` (tabby.tabbyml.com).
+- [Config TOML](https://tabby.tabbyml.com/docs/administration/config-toml)
+  Why it matters: authoritative reference on `Config TOML` (tabby.tabbyml.com).
+- [Upgrade Guide](https://tabby.tabbyml.com/docs/administration/upgrade)
+  Why it matters: authoritative reference on `Upgrade Guide` (tabby.tabbyml.com).
+- [tabby-agent README](https://github.com/TabbyML/tabby/blob/main/clients/tabby-agent/README.md)
+  Why it matters: authoritative reference on `tabby-agent README` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Operations, Upgrades, and Observability](07-operations-upgrades-and-observability.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/teable-database-platform/01-system-overview.md b/tutorials/teable-database-platform/01-system-overview.md
index c22bbc9e..e76b72b7 100644
--- a/tutorials/teable-database-platform/01-system-overview.md
+++ b/tutorials/teable-database-platform/01-system-overview.md
@@ -8,6 +8,9 @@ parent: "Teable Database Platform"
 
 # Chapter 1: System Overview
 
+Welcome to **Chapter 1: System Overview**. In this part of **Teable: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Understanding Teable's multi-dimensional database platform and its approach to modern data management
 
 ## 🎯 Learning Objectives
@@ -838,3 +841,48 @@ This chapter provided the foundation for understanding Teable's multi-dimensiona
 ---
 
 **Ready to explore the database architecture?** Continue to [Chapter 2: Database Architecture](02-database-architecture.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `tableId`, `field`, `name` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: System Overview` as an operating subsystem inside **Teable: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `records`, `fields`, `tables` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: System Overview` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `tableId`.
+2. **Input normalization**: shape incoming data so `field` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `name`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Teable](https://github.com/teableio/teable)
+  Why it matters: authoritative reference on `Teable` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `tableId` and `field` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Database Architecture](02-database-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/teable-database-platform/02-database-architecture.md b/tutorials/teable-database-platform/02-database-architecture.md
index 584fc84d..d7fc9671 100644
--- a/tutorials/teable-database-platform/02-database-architecture.md
+++ b/tutorials/teable-database-platform/02-database-architecture.md
@@ -8,6 +8,9 @@ parent: "Teable Database Platform"
 
 # Chapter 2: Database Architecture
 
+Welcome to **Chapter 2: Database Architecture**. In this part of **Teable: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > PostgreSQL optimization, indexing strategies, and high-performance data operations in Teable
 
 ## 🎯 Learning Objectives
@@ -966,3 +969,49 @@ interface OptimizationSuggestion {
 ---
 
 **Ready for real-time collaboration?** Continue to Chapter 3: Real-Time Collaboration (planned).
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `query`, `plan`, `records` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Database Architecture` as an operating subsystem inside **Teable: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `pool`, `databaseUrl`, `CREATE` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Database Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `query`.
+2. **Input normalization**: shape incoming data so `plan` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `records`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Teable](https://github.com/teableio/teable)
+  Why it matters: authoritative reference on `Teable` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `query` and `plan` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: System Overview](01-system-overview.md)
+- [Next Chapter: Teable Development Environment Setup](03-setup-environment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/teable-database-platform/03-setup-environment.md b/tutorials/teable-database-platform/03-setup-environment.md
index 9d0f9452..9ab76a04 100644
--- a/tutorials/teable-database-platform/03-setup-environment.md
+++ b/tutorials/teable-database-platform/03-setup-environment.md
@@ -8,6 +8,9 @@ parent: "Teable Database Platform"
 
 # Teable Development Environment Setup
 
+Welcome to **Teable Development Environment Setup**. In this part of **Teable: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Prerequisites Overview
 
 ### System Requirements
@@ -569,3 +572,49 @@ Once your development environment is running:
 **✅ Environment Ready? Continue to [System Overview](01-system-overview.md)**
 
 *This setup guide ensures you have a fully functional Teable development environment with all necessary tools and configurations for building scalable database applications.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `prisma`, `teable`, `localhost` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Teable Development Environment Setup` as an operating subsystem inside **Teable: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `docker`, `redis`, `Start` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Teable Development Environment Setup` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `prisma`.
+2. **Input normalization**: shape incoming data so `teable` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `localhost`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Teable](https://github.com/teableio/teable)
+  Why it matters: authoritative reference on `Teable` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `prisma` and `teable` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Database Architecture](02-database-architecture.md)
+- [Next Chapter: Chapter 4: API Development](04-api-development.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/teable-database-platform/04-api-development.md b/tutorials/teable-database-platform/04-api-development.md
index 1f6ed122..bccb6973 100644
--- a/tutorials/teable-database-platform/04-api-development.md
+++ b/tutorials/teable-database-platform/04-api-development.md
@@ -8,6 +8,9 @@ parent: "Teable Database Platform"
 
 # Chapter 4: API Development
 
+Welcome to **Chapter 4: API Development**. In this part of **Teable: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Teable's API layer bridges schema-rich database operations with application-friendly contracts.
 
 ## API Layer Responsibilities
@@ -37,3 +40,49 @@ Teable's API layer bridges schema-rich database operations with application-frie
 You now understand core API-development patterns for reliable Teable integrations.
 
 Next: [Chapter 5: Realtime Collaboration](05-realtime-collaboration.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: API Development` as an operating subsystem inside **Teable: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: API Development` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Teable](https://github.com/teableio/teable)
+  Why it matters: authoritative reference on `Teable` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `API` and `Development` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Teable Development Environment Setup](03-setup-environment.md)
+- [Next Chapter: Chapter 5: Realtime Collaboration](05-realtime-collaboration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/teable-database-platform/05-realtime-collaboration.md b/tutorials/teable-database-platform/05-realtime-collaboration.md
index 6a7908fa..948e300c 100644
--- a/tutorials/teable-database-platform/05-realtime-collaboration.md
+++ b/tutorials/teable-database-platform/05-realtime-collaboration.md
@@ -8,6 +8,9 @@ parent: "Teable Database Platform"
 
 # Chapter 5: Realtime Collaboration
 
+Welcome to **Chapter 5: Realtime Collaboration**. In this part of **Teable: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Realtime collaboration enables low-latency multi-user editing while preserving canonical data consistency.
 
 ## Collaboration Event Flow
@@ -37,3 +40,49 @@ Realtime collaboration enables low-latency multi-user editing while preserving c
 You can now reason about Teable's real-time consistency model under concurrent edits.
 
 Next: [Chapter 6: Query System](06-query-system.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Realtime Collaboration` as an operating subsystem inside **Teable: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Realtime Collaboration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Teable](https://github.com/teableio/teable)
+  Why it matters: authoritative reference on `Teable` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Realtime` and `Collaboration` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: API Development](04-api-development.md)
+- [Next Chapter: Chapter 6: Query System](06-query-system.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/teable-database-platform/06-query-system.md b/tutorials/teable-database-platform/06-query-system.md
index 2ed8568c..32ac09ef 100644
--- a/tutorials/teable-database-platform/06-query-system.md
+++ b/tutorials/teable-database-platform/06-query-system.md
@@ -8,6 +8,9 @@ parent: "Teable Database Platform"
 
 # Chapter 6: Query System
 
+Welcome to **Chapter 6: Query System**. In this part of **Teable: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Teable's query system translates configurable views into performant SQL plans.
 
 ## Query Capabilities
@@ -36,3 +39,49 @@ Teable's query system translates configurable views into performant SQL plans.
 You now understand how Teable balances flexible table UX with predictable query performance.
 
 Next: [Chapter 7: Frontend Architecture](07-frontend-architecture.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Query System` as an operating subsystem inside **Teable: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Query System` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Teable](https://github.com/teableio/teable)
+  Why it matters: authoritative reference on `Teable` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Query` and `System` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Realtime Collaboration](05-realtime-collaboration.md)
+- [Next Chapter: Chapter 7: Frontend Architecture](07-frontend-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/teable-database-platform/07-frontend-architecture.md b/tutorials/teable-database-platform/07-frontend-architecture.md
index 95d95b3b..e44a1e60 100644
--- a/tutorials/teable-database-platform/07-frontend-architecture.md
+++ b/tutorials/teable-database-platform/07-frontend-architecture.md
@@ -8,6 +8,9 @@ parent: "Teable Database Platform"
 
 # Chapter 7: Frontend Architecture
 
+Welcome to **Chapter 7: Frontend Architecture**. In this part of **Teable: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 The frontend must combine schema-driven rendering, editable grids, and real-time state updates.
 
 ## Core Frontend Modules
@@ -37,3 +40,49 @@ The frontend must combine schema-driven rendering, editable grids, and real-time
 You can now navigate Teable frontend responsibilities with a focus on scalability and collaboration correctness.
 
 Next: [Chapter 8: Production Deployment](08-production-deployment.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Frontend Architecture` as an operating subsystem inside **Teable: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Frontend Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Teable](https://github.com/teableio/teable)
+  Why it matters: authoritative reference on `Teable` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Frontend` and `Architecture` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Query System](06-query-system.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/teable-database-platform/08-production-deployment.md b/tutorials/teable-database-platform/08-production-deployment.md
index 7febf533..65a74743 100644
--- a/tutorials/teable-database-platform/08-production-deployment.md
+++ b/tutorials/teable-database-platform/08-production-deployment.md
@@ -8,6 +8,9 @@ parent: "Teable Database Platform"
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **Teable: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Production Teable deployments require coordinated application, database, and realtime operations.
 
 ## Deployment Baseline
@@ -39,3 +42,48 @@ You now have full Teable coverage from architecture to production-grade deployme
 
 Related:
 - [Teable Index](index.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **Teable: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Teable](https://github.com/teableio/teable)
+  Why it matters: authoritative reference on `Teable` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Production` and `Deployment` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Frontend Architecture](07-frontend-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/tiktoken-tutorial/01-getting-started.md b/tutorials/tiktoken-tutorial/01-getting-started.md
index 60de66d3..69bc210d 100644
--- a/tutorials/tiktoken-tutorial/01-getting-started.md
+++ b/tutorials/tiktoken-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: tiktoken Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **tiktoken Tutorial: OpenAI Token Encoding & Optimization**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter introduces tiktoken and gets you productive with basic encode/decode and counting.
 
 ## Install
@@ -60,3 +63,48 @@ print(len(enc.encode("hello world")))
 You now have the core encode/decode workflow and model-specific counting.
 
 Next: [Chapter 2: Tokenization Mechanics](02-tokenization-mechanics.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `tiktoken`, `print`, `venv` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **tiktoken Tutorial: OpenAI Token Encoding & Optimization**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `install`, `text`, `encode` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `tiktoken`.
+2. **Input normalization**: shape incoming data so `print` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `venv`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [tiktoken repository](https://github.com/openai/tiktoken)
+  Why it matters: authoritative reference on `tiktoken repository` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `tiktoken` and `print` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Tokenization Mechanics](02-tokenization-mechanics.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/tiktoken-tutorial/02-tokenization-mechanics.md b/tutorials/tiktoken-tutorial/02-tokenization-mechanics.md
index 8de269a3..c09f1af9 100644
--- a/tutorials/tiktoken-tutorial/02-tokenization-mechanics.md
+++ b/tutorials/tiktoken-tutorial/02-tokenization-mechanics.md
@@ -7,6 +7,9 @@ parent: tiktoken Tutorial
 
 # Chapter 2: Tokenization Mechanics
 
+Welcome to **Chapter 2: Tokenization Mechanics**. In this part of **tiktoken Tutorial: OpenAI Token Encoding & Optimization**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains how BPE tokenization works and why token boundaries look unintuitive.
 
 ## BPE Intuition
@@ -50,3 +53,49 @@ for s in samples:
 You understand how token pieces are formed and how to inspect them.
 
 Next: [Chapter 3: Practical Applications](03-practical-applications.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `token_id`, `naive`, `tiktoken` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Tokenization Mechanics` as an operating subsystem inside **tiktoken Tutorial: OpenAI Token Encoding & Optimization**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `text`, `encode`, `piece` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Tokenization Mechanics` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `token_id`.
+2. **Input normalization**: shape incoming data so `naive` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `tiktoken`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [tiktoken repository](https://github.com/openai/tiktoken)
+  Why it matters: authoritative reference on `tiktoken repository` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `token_id` and `naive` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Practical Applications](03-practical-applications.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/tiktoken-tutorial/03-practical-applications.md b/tutorials/tiktoken-tutorial/03-practical-applications.md
index e7edde9a..0987c3c3 100644
--- a/tutorials/tiktoken-tutorial/03-practical-applications.md
+++ b/tutorials/tiktoken-tutorial/03-practical-applications.md
@@ -7,6 +7,9 @@ parent: tiktoken Tutorial
 
 # Chapter 3: Practical Applications
 
+Welcome to **Chapter 3: Practical Applications**. In this part of **tiktoken Tutorial: OpenAI Token Encoding & Optimization**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Use token counting to manage cost, context limits, and RAG chunking.
 
 ## Cost Estimation
@@ -54,3 +57,49 @@ def token_chunks(text: str, chunk_size: int, overlap: int):
 You can now budget cost, enforce context limits, and chunk by tokens.
 
 Next: [Chapter 4: Educational Module](04-educational-module.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `chunk_size`, `prompt`, `tokens` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Practical Applications` as an operating subsystem inside **tiktoken Tutorial: OpenAI Token Encoding & Optimization**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `encode`, `tiktoken`, `PRICE_PER_1K` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Practical Applications` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `chunk_size`.
+2. **Input normalization**: shape incoming data so `prompt` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `tokens`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [tiktoken repository](https://github.com/openai/tiktoken)
+  Why it matters: authoritative reference on `tiktoken repository` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `chunk_size` and `prompt` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Tokenization Mechanics](02-tokenization-mechanics.md)
+- [Next Chapter: Chapter 4: Educational Module](04-educational-module.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/tiktoken-tutorial/04-educational-module.md b/tutorials/tiktoken-tutorial/04-educational-module.md
index b0802026..4cdfb0e1 100644
--- a/tutorials/tiktoken-tutorial/04-educational-module.md
+++ b/tutorials/tiktoken-tutorial/04-educational-module.md
@@ -7,6 +7,9 @@ parent: tiktoken Tutorial
 
 # Chapter 4: Educational Module
 
+Welcome to **Chapter 4: Educational Module**. In this part of **tiktoken Tutorial: OpenAI Token Encoding & Optimization**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 The educational module helps you visualize and understand tokenization internals.
 
 ## Explore the Educational API
@@ -45,3 +48,49 @@ print(pieces)
 You can now use the educational API to reason about BPE behavior.
 
 Next: [Chapter 5: Optimization Strategies](05-optimization-strategies.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `runbook`, `reliability`, `print` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Educational Module` as an operating subsystem inside **tiktoken Tutorial: OpenAI Token Encoding & Optimization**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `SimpleBytePairEncoding`, `corpus`, `incident` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Educational Module` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `runbook`.
+2. **Input normalization**: shape incoming data so `reliability` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `print`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [tiktoken repository](https://github.com/openai/tiktoken)
+  Why it matters: authoritative reference on `tiktoken repository` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `runbook` and `reliability` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Practical Applications](03-practical-applications.md)
+- [Next Chapter: Chapter 5: Optimization Strategies](05-optimization-strategies.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/tiktoken-tutorial/05-optimization-strategies.md b/tutorials/tiktoken-tutorial/05-optimization-strategies.md
index b82612aa..ef6a7e39 100644
--- a/tutorials/tiktoken-tutorial/05-optimization-strategies.md
+++ b/tutorials/tiktoken-tutorial/05-optimization-strategies.md
@@ -7,6 +7,9 @@ parent: tiktoken Tutorial
 
 # Chapter 5: Optimization Strategies
 
+Welcome to **Chapter 5: Optimization Strategies**. In this part of **tiktoken Tutorial: OpenAI Token Encoding & Optimization**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on performance and operational optimization for token-heavy systems.
 
 ## Strategy 1: Reuse Encoders
@@ -60,3 +63,49 @@ Related:
 - [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
 - [LangChain Tutorial](../langchain-tutorial/)
 - [LlamaIndex Tutorial](../llamaindex-tutorial/)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `text`, `encode`, `tiktoken` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Optimization Strategies` as an operating subsystem inside **tiktoken Tutorial: OpenAI Token Encoding & Optimization**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `lru_cache`, `texts`, `encoding_for_model` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Optimization Strategies` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `text`.
+2. **Input normalization**: shape incoming data so `encode` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `tiktoken`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [tiktoken repository](https://github.com/openai/tiktoken)
+  Why it matters: authoritative reference on `tiktoken repository` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `text` and `encode` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Educational Module](04-educational-module.md)
+- [Next Chapter: Chapter 6: ChatML and Tool Call Accounting](06-chatml-and-tool-calls.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/tiktoken-tutorial/06-chatml-and-tool-calls.md b/tutorials/tiktoken-tutorial/06-chatml-and-tool-calls.md
index fd5526b9..e08913fc 100644
--- a/tutorials/tiktoken-tutorial/06-chatml-and-tool-calls.md
+++ b/tutorials/tiktoken-tutorial/06-chatml-and-tool-calls.md
@@ -7,6 +7,9 @@ parent: tiktoken Tutorial
 
 # Chapter 6: ChatML and Tool Call Accounting
 
+Welcome to **Chapter 6: ChatML and Tool Call Accounting**. In this part of **tiktoken Tutorial: OpenAI Token Encoding & Optimization**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Accurate token accounting for chat and tools is essential for reliability and cost predictability.
 
 ## Where Underestimation Happens
@@ -52,3 +55,49 @@ For tool flows, create separate counters for:
 You can now estimate chat/tool token usage with fewer hidden-cost surprises.
 
 Next: [Chapter 7: Multilingual Tokenization](07-multilingual-tokenization.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `total`, `messages`, `encoding` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: ChatML and Tool Call Accounting` as an operating subsystem inside **tiktoken Tutorial: OpenAI Token Encoding & Optimization**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `fixed_overhead`, `estimate_chat_tokens`, `encode` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: ChatML and Tool Call Accounting` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `total`.
+2. **Input normalization**: shape incoming data so `messages` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `encoding`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [tiktoken repository](https://github.com/openai/tiktoken)
+  Why it matters: authoritative reference on `tiktoken repository` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `total` and `messages` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Optimization Strategies](05-optimization-strategies.md)
+- [Next Chapter: Chapter 7: Multilingual Tokenization](07-multilingual-tokenization.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/tiktoken-tutorial/07-multilingual-tokenization.md b/tutorials/tiktoken-tutorial/07-multilingual-tokenization.md
index bea3fa51..a678570c 100644
--- a/tutorials/tiktoken-tutorial/07-multilingual-tokenization.md
+++ b/tutorials/tiktoken-tutorial/07-multilingual-tokenization.md
@@ -7,6 +7,9 @@ parent: tiktoken Tutorial
 
 # Chapter 7: Multilingual Tokenization
 
+Welcome to **Chapter 7: Multilingual Tokenization**. In this part of **tiktoken Tutorial: OpenAI Token Encoding & Optimization**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Token-per-character ratios vary widely across scripts and languages, so multilingual systems need language-aware budgeting.
 
 ## Why It Matters
@@ -48,3 +51,49 @@ For each locale, track:
 You can now design multilingual prompt systems that are budget-aware and resilient across languages.
 
 Next: [Chapter 8: Cost Governance](08-cost-governance.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Multilingual Tokenization` as an operating subsystem inside **tiktoken Tutorial: OpenAI Token Encoding & Optimization**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Multilingual Tokenization` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [tiktoken repository](https://github.com/openai/tiktoken)
+  Why it matters: authoritative reference on `tiktoken repository` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Multilingual` and `Tokenization` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: ChatML and Tool Call Accounting](06-chatml-and-tool-calls.md)
+- [Next Chapter: Chapter 8: Cost Governance](08-cost-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/tiktoken-tutorial/08-cost-governance.md b/tutorials/tiktoken-tutorial/08-cost-governance.md
index d1c2a44b..f9dcc397 100644
--- a/tutorials/tiktoken-tutorial/08-cost-governance.md
+++ b/tutorials/tiktoken-tutorial/08-cost-governance.md
@@ -7,6 +7,9 @@ parent: tiktoken Tutorial
 
 # Chapter 8: Cost Governance
 
+Welcome to **Chapter 8: Cost Governance**. In this part of **tiktoken Tutorial: OpenAI Token Encoding & Optimization**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter closes with FinOps controls that keep token spend aligned with product value.
 
 ## Governance Framework
@@ -50,3 +53,48 @@ You now have an end-to-end cost-governance playbook for operating tokenized AI s
 Related:
 - [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
 - [LangChain Tutorial](../langchain-tutorial/)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Cost Governance` as an operating subsystem inside **tiktoken Tutorial: OpenAI Token Encoding & Optimization**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Cost Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [tiktoken repository](https://github.com/openai/tiktoken)
+  Why it matters: authoritative reference on `tiktoken repository` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Cost` and `Governance` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Multilingual Tokenization](07-multilingual-tokenization.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/turborepo-tutorial/01-getting-started.md b/tutorials/turborepo-tutorial/01-getting-started.md
index 003acb65..07dc256a 100644
--- a/tutorials/turborepo-tutorial/01-getting-started.md
+++ b/tutorials/turborepo-tutorial/01-getting-started.md
@@ -380,3 +380,48 @@ Now that you understand Turborepo basics, let's explore workspace configuration
 5. Debug and optimize your pipeline execution
 
 *What's your biggest monorepo performance challenge?* ⚡
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `turbo`, `build`, `monorepo` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Turborepo` as an operating subsystem inside **Turborepo Tutorial: High-Performance Monorepo Build System**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `packages`, `json`, `package` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Turborepo` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `turbo`.
+2. **Input normalization**: shape incoming data so `build` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `monorepo`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/vercel/turborepo)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `turbo` and `build` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Workspace Configuration](02-workspace-configuration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/turborepo-tutorial/02-workspace-configuration.md b/tutorials/turborepo-tutorial/02-workspace-configuration.md
index e49f5dd3..e42e3dd6 100644
--- a/tutorials/turborepo-tutorial/02-workspace-configuration.md
+++ b/tutorials/turborepo-tutorial/02-workspace-configuration.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Workspace Configuration
 
+Welcome to **Chapter 2: Workspace Configuration**. In this part of **Turborepo Tutorial: High-Performance Monorepo Build System**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Learn how to configure Turborepo workspaces, manage dependencies, and set up your monorepo structure for optimal performance.
 
 ## Workspace Setup
@@ -268,3 +271,49 @@ With your workspace configured, let's explore how to define and run build pipeli
 ---
 
 *Generated by [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `build`, `dependsOn`, `json` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Workspace Configuration` as an operating subsystem inside **Turborepo Tutorial: High-Performance Monorepo Build System**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `turbo`, `next`, `test` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Workspace Configuration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `build`.
+2. **Input normalization**: shape incoming data so `dependsOn` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `json`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/vercel/turborepo)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `build` and `dependsOn` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Turborepo](01-getting-started.md)
+- [Next Chapter: Chapter 3: Task Pipelines](03-task-pipelines.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/turborepo-tutorial/03-task-pipelines.md b/tutorials/turborepo-tutorial/03-task-pipelines.md
index d47d3436..bff9cccc 100644
--- a/tutorials/turborepo-tutorial/03-task-pipelines.md
+++ b/tutorials/turborepo-tutorial/03-task-pipelines.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Task Pipelines
 
+Welcome to **Chapter 3: Task Pipelines**. In this part of **Turborepo Tutorial: High-Performance Monorepo Build System**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Master Turborepo's task pipeline system for efficient build orchestration and dependency management. Task pipelines are the heart of Turborepo -- they define what runs, in what order, and how tasks relate to each other across your monorepo. A well-designed pipeline ensures that builds are fast, correct, and reproducible.
 
 ## How Task Pipelines Work
@@ -673,3 +676,49 @@ With your task pipelines defined, the next critical optimization is caching. In
 ---
 
 *Built with insights from the [Turborepo](https://github.com/vercel/turborepo) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `build`, `dependsOn`, `turbo` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Task Pipelines` as an operating subsystem inside **Turborepo Tutorial: High-Performance Monorepo Build System**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `repo`, `test`, `outputs` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Task Pipelines` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `build`.
+2. **Input normalization**: shape incoming data so `dependsOn` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `turbo`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/vercel/turborepo)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `build` and `dependsOn` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Workspace Configuration](02-workspace-configuration.md)
+- [Next Chapter: Chapter 4: Caching Strategies](04-caching-strategies.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/turborepo-tutorial/04-caching-strategies.md b/tutorials/turborepo-tutorial/04-caching-strategies.md
index cd017104..27d26e18 100644
--- a/tutorials/turborepo-tutorial/04-caching-strategies.md
+++ b/tutorials/turborepo-tutorial/04-caching-strategies.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Caching Strategies
 
+Welcome to **Chapter 4: Caching Strategies**. In this part of **Turborepo Tutorial: High-Performance Monorepo Build System**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Learn Turborepo's intelligent caching system that dramatically speeds up your builds by avoiding redundant work. Caching is the single most impactful feature of Turborepo -- it transforms multi-minute builds into sub-second cache restores by fingerprinting your tasks and storing their outputs.
 
 ## How Caching Works
@@ -512,3 +515,49 @@ Local caching is powerful, but the real force multiplier is sharing cache artifa
 ---
 
 *Built with insights from the [Turborepo](https://github.com/vercel/turborepo) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `build`, `turbo`, `cache` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Caching Strategies` as an operating subsystem inside **Turborepo Tutorial: High-Performance Monorepo Build System**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `outputs`, `json`, `classDef` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Caching Strategies` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `build`.
+2. **Input normalization**: shape incoming data so `turbo` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `cache`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/vercel/turborepo)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `build` and `turbo` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Task Pipelines](03-task-pipelines.md)
+- [Next Chapter: Chapter 5: Remote Caching](05-remote-caching.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/turborepo-tutorial/05-remote-caching.md b/tutorials/turborepo-tutorial/05-remote-caching.md
index 29b0228c..fb0b31ab 100644
--- a/tutorials/turborepo-tutorial/05-remote-caching.md
+++ b/tutorials/turborepo-tutorial/05-remote-caching.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Remote Caching
 
+Welcome to **Chapter 5: Remote Caching**. In this part of **Turborepo Tutorial: High-Performance Monorepo Build System**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Set up team-wide caching to share build artifacts across your organization and CI/CD pipelines. While local caching eliminates redundant work on a single machine, remote caching extends that benefit across every developer workstation and every CI runner -- so a build that one teammate already completed becomes an instant cache restore for everyone else.
 
 ## Why Remote Caching Matters
@@ -577,3 +580,49 @@ With caching optimized both locally and remotely, the next challenge is managing
 ---
 
 *Built with insights from the [Turborepo](https://github.com/vercel/turborepo) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `build`, `cache`, `turbo` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Remote Caching` as an operating subsystem inside **Turborepo Tutorial: High-Performance Monorepo Build System**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `artifacts`, `your`, `classDef` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Remote Caching` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `build`.
+2. **Input normalization**: shape incoming data so `cache` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `turbo`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/vercel/turborepo)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `build` and `cache` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Caching Strategies](04-caching-strategies.md)
+- [Next Chapter: Chapter 6: Dependency Management](06-dependency-management.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/turborepo-tutorial/06-dependency-management.md b/tutorials/turborepo-tutorial/06-dependency-management.md
index de1c71d9..7e1bcc0e 100644
--- a/tutorials/turborepo-tutorial/06-dependency-management.md
+++ b/tutorials/turborepo-tutorial/06-dependency-management.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Dependency Management
 
+Welcome to **Chapter 6: Dependency Management**. In this part of **Turborepo Tutorial: High-Performance Monorepo Build System**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Manage internal and external dependencies efficiently in your Turborepo monorepo. A well-structured dependency graph is the foundation of a healthy monorepo -- it determines build order, affects cache performance, and defines the boundaries between your packages. This chapter covers workspace references, dependency hoisting, version management, and the powerful `turbo prune` command for creating lean, deployable subsets of your monorepo.
 
 ## Understanding the Dependency Graph
@@ -602,3 +605,49 @@ With your dependency graph well-structured and your deployment pipeline streamli
 ---
 
 *Built with insights from the [Turborepo](https://github.com/vercel/turborepo) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `repo`, `json`, `react` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Dependency Management` as an operating subsystem inside **Turborepo Tutorial: High-Performance Monorepo Build System**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `packages`, `turbo`, `classDef` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Dependency Management` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `repo`.
+2. **Input normalization**: shape incoming data so `json` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `react`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/vercel/turborepo)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `repo` and `json` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Remote Caching](05-remote-caching.md)
+- [Next Chapter: Chapter 7: CI/CD Integration](07-cicd-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/turborepo-tutorial/07-cicd-integration.md b/tutorials/turborepo-tutorial/07-cicd-integration.md
index 7fdba712..045ec7c1 100644
--- a/tutorials/turborepo-tutorial/07-cicd-integration.md
+++ b/tutorials/turborepo-tutorial/07-cicd-integration.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: CI/CD Integration
 
+Welcome to **Chapter 7: CI/CD Integration**. In this part of **Turborepo Tutorial: High-Performance Monorepo Build System**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Integrate Turborepo with your CI/CD pipelines for automated building, testing, and deployment. Continuous integration is where Turborepo's caching and parallel execution deliver the most dramatic improvements -- transforming 30-minute pipeline runs into 3-minute ones by caching previously built packages and only rebuilding what changed.
 
 ## CI/CD Strategy Overview
@@ -802,3 +805,49 @@ With your CI/CD pipeline fully optimized, the final chapter covers advanced perf
 ---
 
 *Built with insights from the [Turborepo](https://github.com/vercel/turborepo) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `pnpm`, `name`, `uses` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: CI/CD Integration` as an operating subsystem inside **Turborepo Tutorial: High-Performance Monorepo Build System**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `turbo`, `install`, `build` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: CI/CD Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `pnpm`.
+2. **Input normalization**: shape incoming data so `name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `uses`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/vercel/turborepo)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `pnpm` and `name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Dependency Management](06-dependency-management.md)
+- [Next Chapter: Chapter 8: Performance Optimization](08-performance-optimization.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/turborepo-tutorial/08-performance-optimization.md b/tutorials/turborepo-tutorial/08-performance-optimization.md
index fcdb3f32..02445a9c 100644
--- a/tutorials/turborepo-tutorial/08-performance-optimization.md
+++ b/tutorials/turborepo-tutorial/08-performance-optimization.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Performance Optimization
 
+Welcome to **Chapter 8: Performance Optimization**. In this part of **Turborepo Tutorial: High-Performance Monorepo Build System**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Advanced techniques for maximizing Turborepo performance in large-scale monorepos. As your monorepo grows from a handful of packages to hundreds, maintaining fast build times requires deliberate optimization. This chapter covers profiling, parallelism tuning, graph optimization, package architecture, and the monitoring practices that keep your builds fast over time.
 
 ## Profiling Build Performance
@@ -768,3 +771,48 @@ Congratulations on completing the Turborepo tutorial. You have learned to:
 ---
 
 *Built with insights from the [Turborepo](https://github.com/vercel/turborepo) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `build`, `turbo`, `json` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Performance Optimization` as an operating subsystem inside **Turborepo Tutorial: High-Performance Monorepo Build System**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `tasks`, `cache`, `classDef` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Performance Optimization` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `build`.
+2. **Input normalization**: shape incoming data so `turbo` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `json`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/vercel/turborepo)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `build` and `turbo` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: CI/CD Integration](07-cicd-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/use-mcp-tutorial/01-getting-started-and-archived-context.md b/tutorials/use-mcp-tutorial/01-getting-started-and-archived-context.md
index 49e6b901..6df6ce8f 100644
--- a/tutorials/use-mcp-tutorial/01-getting-started-and-archived-context.md
+++ b/tutorials/use-mcp-tutorial/01-getting-started-and-archived-context.md
@@ -7,6 +7,9 @@ parent: use-mcp Tutorial
 
 # Chapter 1: Getting Started and Archived Context
 
+Welcome to **Chapter 1: Getting Started and Archived Context**. In this part of **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter establishes baseline setup and risk framing for an archived dependency.
 
 ## Learning Goals
@@ -34,3 +37,610 @@ Treat initial adoption as an integration reference layer and keep migration opti
 You now have a setup and risk baseline for evaluating `use-mcp` usage.
 
 Next: [Chapter 2: Hook Architecture and Connection Lifecycle](02-hook-architecture-and-connection-lifecycle.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- tutorial slug: **use-mcp-tutorial**
+- chapter focus: **Chapter 1: Getting Started and Archived Context**
+- system context: **Use Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started and Archived Context`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [use-mcp README](https://github.com/modelcontextprotocol/use-mcp/blob/main/README.md)
+- [use-mcp React Integration](https://github.com/modelcontextprotocol/use-mcp/blob/main/src/react/README.md)
+- [Inspector Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/inspector/README.md)
+- [Chat UI Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/chat-ui/README.md)
+- [Cloudflare Agents Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/cf-agents/README.md)
+- [Hono MCP Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/hono-mcp/README.md)
+- [Integration Test Guide](https://github.com/modelcontextprotocol/use-mcp/blob/main/test/README.md)
+- [Project Guidelines](https://github.com/modelcontextprotocol/use-mcp/blob/main/AGENT.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP TypeScript SDK Tutorial](../mcp-typescript-sdk-tutorial/)
+- [MCP Use Tutorial](../mcp-use-tutorial/)
+- [MCP Ext Apps Tutorial](../mcp-ext-apps-tutorial/)
+- [MCP Inspector Tutorial](../mcp-inspector-tutorial/)
+- [Chapter 1: Getting Started and Archived Context](01-getting-started-and-archived-context.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started and Archived Context`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started and Archived Context
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `install` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started and Archived Context` as an operating subsystem inside **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started and Archived Context` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `install`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [use-mcp README](https://github.com/modelcontextprotocol/use-mcp/blob/main/README.md)
+  Why it matters: authoritative reference on `use-mcp README` (github.com).
+- [use-mcp React Integration](https://github.com/modelcontextprotocol/use-mcp/blob/main/src/react/README.md)
+  Why it matters: authoritative reference on `use-mcp React Integration` (github.com).
+- [Inspector Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/inspector/README.md)
+  Why it matters: authoritative reference on `Inspector Example` (github.com).
+- [Chat UI Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/chat-ui/README.md)
+  Why it matters: authoritative reference on `Chat UI Example` (github.com).
+- [Cloudflare Agents Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/cf-agents/README.md)
+  Why it matters: authoritative reference on `Cloudflare Agents Example` (github.com).
+- [Hono MCP Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/hono-mcp/README.md)
+  Why it matters: authoritative reference on `Hono MCP Example` (github.com).
+- [Integration Test Guide](https://github.com/modelcontextprotocol/use-mcp/blob/main/test/README.md)
+  Why it matters: authoritative reference on `Integration Test Guide` (github.com).
+- [Project Guidelines](https://github.com/modelcontextprotocol/use-mcp/blob/main/AGENT.md)
+  Why it matters: authoritative reference on `Project Guidelines` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `install` and `install` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Hook Architecture and Connection Lifecycle](02-hook-architecture-and-connection-lifecycle.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/use-mcp-tutorial/02-hook-architecture-and-connection-lifecycle.md b/tutorials/use-mcp-tutorial/02-hook-architecture-and-connection-lifecycle.md
index 0a67621f..aef159cd 100644
--- a/tutorials/use-mcp-tutorial/02-hook-architecture-and-connection-lifecycle.md
+++ b/tutorials/use-mcp-tutorial/02-hook-architecture-and-connection-lifecycle.md
@@ -7,6 +7,9 @@ parent: use-mcp Tutorial
 
 # Chapter 2: Hook Architecture and Connection Lifecycle
 
+Welcome to **Chapter 2: Hook Architecture and Connection Lifecycle**. In this part of **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains core `useMcp` lifecycle and state transitions.
 
 ## Learning Goals
@@ -35,3 +38,607 @@ This chapter explains core `useMcp` lifecycle and state transitions.
 You now have a lifecycle model for robust hook-driven MCP client UX.
 
 Next: [Chapter 3: Authentication, OAuth Callback, and Storage](03-authentication-oauth-callback-and-storage.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- tutorial slug: **use-mcp-tutorial**
+- chapter focus: **Chapter 2: Hook Architecture and Connection Lifecycle**
+- system context: **Use Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Hook Architecture and Connection Lifecycle`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [use-mcp README](https://github.com/modelcontextprotocol/use-mcp/blob/main/README.md)
+- [use-mcp React Integration](https://github.com/modelcontextprotocol/use-mcp/blob/main/src/react/README.md)
+- [Inspector Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/inspector/README.md)
+- [Chat UI Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/chat-ui/README.md)
+- [Cloudflare Agents Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/cf-agents/README.md)
+- [Hono MCP Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/hono-mcp/README.md)
+- [Integration Test Guide](https://github.com/modelcontextprotocol/use-mcp/blob/main/test/README.md)
+- [Project Guidelines](https://github.com/modelcontextprotocol/use-mcp/blob/main/AGENT.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP TypeScript SDK Tutorial](../mcp-typescript-sdk-tutorial/)
+- [MCP Use Tutorial](../mcp-use-tutorial/)
+- [MCP Ext Apps Tutorial](../mcp-ext-apps-tutorial/)
+- [MCP Inspector Tutorial](../mcp-inspector-tutorial/)
+- [Chapter 1: Getting Started and Archived Context](01-getting-started-and-archived-context.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Hook Architecture and Connection Lifecycle`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Hook Architecture and Connection Lifecycle
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Hook Architecture and Connection Lifecycle` as an operating subsystem inside **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Hook Architecture and Connection Lifecycle` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [use-mcp README](https://github.com/modelcontextprotocol/use-mcp/blob/main/README.md)
+  Why it matters: authoritative reference on `use-mcp README` (github.com).
+- [use-mcp React Integration](https://github.com/modelcontextprotocol/use-mcp/blob/main/src/react/README.md)
+  Why it matters: authoritative reference on `use-mcp React Integration` (github.com).
+- [Inspector Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/inspector/README.md)
+  Why it matters: authoritative reference on `Inspector Example` (github.com).
+- [Chat UI Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/chat-ui/README.md)
+  Why it matters: authoritative reference on `Chat UI Example` (github.com).
+- [Cloudflare Agents Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/cf-agents/README.md)
+  Why it matters: authoritative reference on `Cloudflare Agents Example` (github.com).
+- [Hono MCP Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/hono-mcp/README.md)
+  Why it matters: authoritative reference on `Hono MCP Example` (github.com).
+- [Integration Test Guide](https://github.com/modelcontextprotocol/use-mcp/blob/main/test/README.md)
+  Why it matters: authoritative reference on `Integration Test Guide` (github.com).
+- [Project Guidelines](https://github.com/modelcontextprotocol/use-mcp/blob/main/AGENT.md)
+  Why it matters: authoritative reference on `Project Guidelines` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started and Archived Context](01-getting-started-and-archived-context.md)
+- [Next Chapter: Chapter 3: Authentication, OAuth Callback, and Storage](03-authentication-oauth-callback-and-storage.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/use-mcp-tutorial/03-authentication-oauth-callback-and-storage.md b/tutorials/use-mcp-tutorial/03-authentication-oauth-callback-and-storage.md
index f21c53c8..0ed46968 100644
--- a/tutorials/use-mcp-tutorial/03-authentication-oauth-callback-and-storage.md
+++ b/tutorials/use-mcp-tutorial/03-authentication-oauth-callback-and-storage.md
@@ -7,6 +7,9 @@ parent: use-mcp Tutorial
 
 # Chapter 3: Authentication, OAuth Callback, and Storage
 
+Welcome to **Chapter 3: Authentication, OAuth Callback, and Storage**. In this part of **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers auth design details that most often fail in browser MCP integrations.
 
 ## Learning Goals
@@ -33,3 +36,607 @@ This chapter covers auth design details that most often fail in browser MCP inte
 You now have a safer OAuth and auth-state handling model for React MCP clients.
 
 Next: [Chapter 4: Tools, Resources, Prompts, and Client Operations](04-tools-resources-prompts-and-client-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- tutorial slug: **use-mcp-tutorial**
+- chapter focus: **Chapter 3: Authentication, OAuth Callback, and Storage**
+- system context: **Use Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Authentication, OAuth Callback, and Storage`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [use-mcp README](https://github.com/modelcontextprotocol/use-mcp/blob/main/README.md)
+- [use-mcp React Integration](https://github.com/modelcontextprotocol/use-mcp/blob/main/src/react/README.md)
+- [Inspector Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/inspector/README.md)
+- [Chat UI Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/chat-ui/README.md)
+- [Cloudflare Agents Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/cf-agents/README.md)
+- [Hono MCP Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/hono-mcp/README.md)
+- [Integration Test Guide](https://github.com/modelcontextprotocol/use-mcp/blob/main/test/README.md)
+- [Project Guidelines](https://github.com/modelcontextprotocol/use-mcp/blob/main/AGENT.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP TypeScript SDK Tutorial](../mcp-typescript-sdk-tutorial/)
+- [MCP Use Tutorial](../mcp-use-tutorial/)
+- [MCP Ext Apps Tutorial](../mcp-ext-apps-tutorial/)
+- [MCP Inspector Tutorial](../mcp-inspector-tutorial/)
+- [Chapter 1: Getting Started and Archived Context](01-getting-started-and-archived-context.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Authentication, OAuth Callback, and Storage`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Authentication, OAuth Callback, and Storage
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Authentication, OAuth Callback, and Storage` as an operating subsystem inside **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Authentication, OAuth Callback, and Storage` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [use-mcp README](https://github.com/modelcontextprotocol/use-mcp/blob/main/README.md)
+  Why it matters: authoritative reference on `use-mcp README` (github.com).
+- [use-mcp React Integration](https://github.com/modelcontextprotocol/use-mcp/blob/main/src/react/README.md)
+  Why it matters: authoritative reference on `use-mcp React Integration` (github.com).
+- [Inspector Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/inspector/README.md)
+  Why it matters: authoritative reference on `Inspector Example` (github.com).
+- [Chat UI Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/chat-ui/README.md)
+  Why it matters: authoritative reference on `Chat UI Example` (github.com).
+- [Cloudflare Agents Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/cf-agents/README.md)
+  Why it matters: authoritative reference on `Cloudflare Agents Example` (github.com).
+- [Hono MCP Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/hono-mcp/README.md)
+  Why it matters: authoritative reference on `Hono MCP Example` (github.com).
+- [Integration Test Guide](https://github.com/modelcontextprotocol/use-mcp/blob/main/test/README.md)
+  Why it matters: authoritative reference on `Integration Test Guide` (github.com).
+- [Project Guidelines](https://github.com/modelcontextprotocol/use-mcp/blob/main/AGENT.md)
+  Why it matters: authoritative reference on `Project Guidelines` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Hook Architecture and Connection Lifecycle](02-hook-architecture-and-connection-lifecycle.md)
+- [Next Chapter: Chapter 4: Tools, Resources, Prompts, and Client Operations](04-tools-resources-prompts-and-client-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/use-mcp-tutorial/04-tools-resources-prompts-and-client-operations.md b/tutorials/use-mcp-tutorial/04-tools-resources-prompts-and-client-operations.md
index 7825a53f..1a0a354b 100644
--- a/tutorials/use-mcp-tutorial/04-tools-resources-prompts-and-client-operations.md
+++ b/tutorials/use-mcp-tutorial/04-tools-resources-prompts-and-client-operations.md
@@ -7,6 +7,9 @@ parent: use-mcp Tutorial
 
 # Chapter 4: Tools, Resources, Prompts, and Client Operations
 
+Welcome to **Chapter 4: Tools, Resources, Prompts, and Client Operations**. In this part of **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter maps core MCP capability access into reusable React operations.
 
 ## Learning Goals
@@ -34,3 +37,607 @@ This chapter maps core MCP capability access into reusable React operations.
 You now have an operations model for integrating MCP capabilities into React workflows.
 
 Next: [Chapter 5: Transport, Retry, and Reconnect Strategy](05-transport-retry-and-reconnect-strategy.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- tutorial slug: **use-mcp-tutorial**
+- chapter focus: **Chapter 4: Tools, Resources, Prompts, and Client Operations**
+- system context: **Use Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Tools, Resources, Prompts, and Client Operations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [use-mcp README](https://github.com/modelcontextprotocol/use-mcp/blob/main/README.md)
+- [use-mcp React Integration](https://github.com/modelcontextprotocol/use-mcp/blob/main/src/react/README.md)
+- [Inspector Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/inspector/README.md)
+- [Chat UI Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/chat-ui/README.md)
+- [Cloudflare Agents Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/cf-agents/README.md)
+- [Hono MCP Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/hono-mcp/README.md)
+- [Integration Test Guide](https://github.com/modelcontextprotocol/use-mcp/blob/main/test/README.md)
+- [Project Guidelines](https://github.com/modelcontextprotocol/use-mcp/blob/main/AGENT.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP TypeScript SDK Tutorial](../mcp-typescript-sdk-tutorial/)
+- [MCP Use Tutorial](../mcp-use-tutorial/)
+- [MCP Ext Apps Tutorial](../mcp-ext-apps-tutorial/)
+- [MCP Inspector Tutorial](../mcp-inspector-tutorial/)
+- [Chapter 1: Getting Started and Archived Context](01-getting-started-and-archived-context.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Tools, Resources, Prompts, and Client Operations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Tools, Resources, Prompts, and Client Operations
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Tools, Resources, Prompts, and Client Operations` as an operating subsystem inside **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Tools, Resources, Prompts, and Client Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [use-mcp README](https://github.com/modelcontextprotocol/use-mcp/blob/main/README.md)
+  Why it matters: authoritative reference on `use-mcp README` (github.com).
+- [use-mcp React Integration](https://github.com/modelcontextprotocol/use-mcp/blob/main/src/react/README.md)
+  Why it matters: authoritative reference on `use-mcp React Integration` (github.com).
+- [Inspector Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/inspector/README.md)
+  Why it matters: authoritative reference on `Inspector Example` (github.com).
+- [Chat UI Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/chat-ui/README.md)
+  Why it matters: authoritative reference on `Chat UI Example` (github.com).
+- [Cloudflare Agents Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/cf-agents/README.md)
+  Why it matters: authoritative reference on `Cloudflare Agents Example` (github.com).
+- [Hono MCP Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/hono-mcp/README.md)
+  Why it matters: authoritative reference on `Hono MCP Example` (github.com).
+- [Integration Test Guide](https://github.com/modelcontextprotocol/use-mcp/blob/main/test/README.md)
+  Why it matters: authoritative reference on `Integration Test Guide` (github.com).
+- [Project Guidelines](https://github.com/modelcontextprotocol/use-mcp/blob/main/AGENT.md)
+  Why it matters: authoritative reference on `Project Guidelines` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Authentication, OAuth Callback, and Storage](03-authentication-oauth-callback-and-storage.md)
+- [Next Chapter: Chapter 5: Transport, Retry, and Reconnect Strategy](05-transport-retry-and-reconnect-strategy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/use-mcp-tutorial/05-transport-retry-and-reconnect-strategy.md b/tutorials/use-mcp-tutorial/05-transport-retry-and-reconnect-strategy.md
index 8fe28e6b..36d528de 100644
--- a/tutorials/use-mcp-tutorial/05-transport-retry-and-reconnect-strategy.md
+++ b/tutorials/use-mcp-tutorial/05-transport-retry-and-reconnect-strategy.md
@@ -7,6 +7,9 @@ parent: use-mcp Tutorial
 
 # Chapter 5: Transport, Retry, and Reconnect Strategy
 
+Welcome to **Chapter 5: Transport, Retry, and Reconnect Strategy**. In this part of **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on resilience controls for unstable networks and intermittent server availability.
 
 ## Learning Goals
@@ -33,3 +36,607 @@ This chapter focuses on resilience controls for unstable networks and intermitte
 You now have a practical resilience model for browser-based MCP client sessions.
 
 Next: [Chapter 6: React Integration Patterns: Chat UI and Inspector](06-react-integration-patterns-chat-ui-and-inspector.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- tutorial slug: **use-mcp-tutorial**
+- chapter focus: **Chapter 5: Transport, Retry, and Reconnect Strategy**
+- system context: **Use Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Transport, Retry, and Reconnect Strategy`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [use-mcp README](https://github.com/modelcontextprotocol/use-mcp/blob/main/README.md)
+- [use-mcp React Integration](https://github.com/modelcontextprotocol/use-mcp/blob/main/src/react/README.md)
+- [Inspector Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/inspector/README.md)
+- [Chat UI Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/chat-ui/README.md)
+- [Cloudflare Agents Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/cf-agents/README.md)
+- [Hono MCP Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/hono-mcp/README.md)
+- [Integration Test Guide](https://github.com/modelcontextprotocol/use-mcp/blob/main/test/README.md)
+- [Project Guidelines](https://github.com/modelcontextprotocol/use-mcp/blob/main/AGENT.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP TypeScript SDK Tutorial](../mcp-typescript-sdk-tutorial/)
+- [MCP Use Tutorial](../mcp-use-tutorial/)
+- [MCP Ext Apps Tutorial](../mcp-ext-apps-tutorial/)
+- [MCP Inspector Tutorial](../mcp-inspector-tutorial/)
+- [Chapter 1: Getting Started and Archived Context](01-getting-started-and-archived-context.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Transport, Retry, and Reconnect Strategy`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Transport, Retry, and Reconnect Strategy
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Transport, Retry, and Reconnect Strategy` as an operating subsystem inside **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Transport, Retry, and Reconnect Strategy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [use-mcp README](https://github.com/modelcontextprotocol/use-mcp/blob/main/README.md)
+  Why it matters: authoritative reference on `use-mcp README` (github.com).
+- [use-mcp React Integration](https://github.com/modelcontextprotocol/use-mcp/blob/main/src/react/README.md)
+  Why it matters: authoritative reference on `use-mcp React Integration` (github.com).
+- [Inspector Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/inspector/README.md)
+  Why it matters: authoritative reference on `Inspector Example` (github.com).
+- [Chat UI Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/chat-ui/README.md)
+  Why it matters: authoritative reference on `Chat UI Example` (github.com).
+- [Cloudflare Agents Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/cf-agents/README.md)
+  Why it matters: authoritative reference on `Cloudflare Agents Example` (github.com).
+- [Hono MCP Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/hono-mcp/README.md)
+  Why it matters: authoritative reference on `Hono MCP Example` (github.com).
+- [Integration Test Guide](https://github.com/modelcontextprotocol/use-mcp/blob/main/test/README.md)
+  Why it matters: authoritative reference on `Integration Test Guide` (github.com).
+- [Project Guidelines](https://github.com/modelcontextprotocol/use-mcp/blob/main/AGENT.md)
+  Why it matters: authoritative reference on `Project Guidelines` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Tools, Resources, Prompts, and Client Operations](04-tools-resources-prompts-and-client-operations.md)
+- [Next Chapter: Chapter 6: React Integration Patterns: Chat UI and Inspector](06-react-integration-patterns-chat-ui-and-inspector.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/use-mcp-tutorial/06-react-integration-patterns-chat-ui-and-inspector.md b/tutorials/use-mcp-tutorial/06-react-integration-patterns-chat-ui-and-inspector.md
index ec9d7e53..bd631cb2 100644
--- a/tutorials/use-mcp-tutorial/06-react-integration-patterns-chat-ui-and-inspector.md
+++ b/tutorials/use-mcp-tutorial/06-react-integration-patterns-chat-ui-and-inspector.md
@@ -7,6 +7,9 @@ parent: use-mcp Tutorial
 
 # Chapter 6: React Integration Patterns: Chat UI and Inspector
 
+Welcome to **Chapter 6: React Integration Patterns: Chat UI and Inspector**. In this part of **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter extracts reusable architecture patterns from official example apps.
 
 ## Learning Goals
@@ -34,3 +37,607 @@ This chapter extracts reusable architecture patterns from official example apps.
 You now have an example-driven component architecture model for MCP-enabled React apps.
 
 Next: [Chapter 7: Testing, Debugging, and Integration Servers](07-testing-debugging-and-integration-servers.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- tutorial slug: **use-mcp-tutorial**
+- chapter focus: **Chapter 6: React Integration Patterns: Chat UI and Inspector**
+- system context: **Use Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: React Integration Patterns: Chat UI and Inspector`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [use-mcp README](https://github.com/modelcontextprotocol/use-mcp/blob/main/README.md)
+- [use-mcp React Integration](https://github.com/modelcontextprotocol/use-mcp/blob/main/src/react/README.md)
+- [Inspector Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/inspector/README.md)
+- [Chat UI Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/chat-ui/README.md)
+- [Cloudflare Agents Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/cf-agents/README.md)
+- [Hono MCP Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/hono-mcp/README.md)
+- [Integration Test Guide](https://github.com/modelcontextprotocol/use-mcp/blob/main/test/README.md)
+- [Project Guidelines](https://github.com/modelcontextprotocol/use-mcp/blob/main/AGENT.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP TypeScript SDK Tutorial](../mcp-typescript-sdk-tutorial/)
+- [MCP Use Tutorial](../mcp-use-tutorial/)
+- [MCP Ext Apps Tutorial](../mcp-ext-apps-tutorial/)
+- [MCP Inspector Tutorial](../mcp-inspector-tutorial/)
+- [Chapter 1: Getting Started and Archived Context](01-getting-started-and-archived-context.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: React Integration Patterns: Chat UI and Inspector`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: React Integration Patterns: Chat UI and Inspector
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: React Integration Patterns: Chat UI and Inspector` as an operating subsystem inside **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: React Integration Patterns: Chat UI and Inspector` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [use-mcp README](https://github.com/modelcontextprotocol/use-mcp/blob/main/README.md)
+  Why it matters: authoritative reference on `use-mcp README` (github.com).
+- [use-mcp React Integration](https://github.com/modelcontextprotocol/use-mcp/blob/main/src/react/README.md)
+  Why it matters: authoritative reference on `use-mcp React Integration` (github.com).
+- [Inspector Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/inspector/README.md)
+  Why it matters: authoritative reference on `Inspector Example` (github.com).
+- [Chat UI Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/chat-ui/README.md)
+  Why it matters: authoritative reference on `Chat UI Example` (github.com).
+- [Cloudflare Agents Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/cf-agents/README.md)
+  Why it matters: authoritative reference on `Cloudflare Agents Example` (github.com).
+- [Hono MCP Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/hono-mcp/README.md)
+  Why it matters: authoritative reference on `Hono MCP Example` (github.com).
+- [Integration Test Guide](https://github.com/modelcontextprotocol/use-mcp/blob/main/test/README.md)
+  Why it matters: authoritative reference on `Integration Test Guide` (github.com).
+- [Project Guidelines](https://github.com/modelcontextprotocol/use-mcp/blob/main/AGENT.md)
+  Why it matters: authoritative reference on `Project Guidelines` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Transport, Retry, and Reconnect Strategy](05-transport-retry-and-reconnect-strategy.md)
+- [Next Chapter: Chapter 7: Testing, Debugging, and Integration Servers](07-testing-debugging-and-integration-servers.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/use-mcp-tutorial/07-testing-debugging-and-integration-servers.md b/tutorials/use-mcp-tutorial/07-testing-debugging-and-integration-servers.md
index a83fa94c..5746be92 100644
--- a/tutorials/use-mcp-tutorial/07-testing-debugging-and-integration-servers.md
+++ b/tutorials/use-mcp-tutorial/07-testing-debugging-and-integration-servers.md
@@ -7,6 +7,9 @@ parent: use-mcp Tutorial
 
 # Chapter 7: Testing, Debugging, and Integration Servers
 
+Welcome to **Chapter 7: Testing, Debugging, and Integration Servers**. In this part of **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter defines validation strategies for MCP client correctness and regression safety.
 
 ## Learning Goals
@@ -33,3 +36,607 @@ This chapter defines validation strategies for MCP client correctness and regres
 You now have a repeatable verification framework for `use-mcp` integrations.
 
 Next: [Chapter 8: Maintenance Risk, Migration, and Production Guidance](08-maintenance-risk-migration-and-production-guidance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- tutorial slug: **use-mcp-tutorial**
+- chapter focus: **Chapter 7: Testing, Debugging, and Integration Servers**
+- system context: **Use Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Testing, Debugging, and Integration Servers`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [use-mcp README](https://github.com/modelcontextprotocol/use-mcp/blob/main/README.md)
+- [use-mcp React Integration](https://github.com/modelcontextprotocol/use-mcp/blob/main/src/react/README.md)
+- [Inspector Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/inspector/README.md)
+- [Chat UI Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/chat-ui/README.md)
+- [Cloudflare Agents Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/cf-agents/README.md)
+- [Hono MCP Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/hono-mcp/README.md)
+- [Integration Test Guide](https://github.com/modelcontextprotocol/use-mcp/blob/main/test/README.md)
+- [Project Guidelines](https://github.com/modelcontextprotocol/use-mcp/blob/main/AGENT.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP TypeScript SDK Tutorial](../mcp-typescript-sdk-tutorial/)
+- [MCP Use Tutorial](../mcp-use-tutorial/)
+- [MCP Ext Apps Tutorial](../mcp-ext-apps-tutorial/)
+- [MCP Inspector Tutorial](../mcp-inspector-tutorial/)
+- [Chapter 1: Getting Started and Archived Context](01-getting-started-and-archived-context.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Testing, Debugging, and Integration Servers`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Testing, Debugging, and Integration Servers
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Testing, Debugging, and Integration Servers` as an operating subsystem inside **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Testing, Debugging, and Integration Servers` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [use-mcp README](https://github.com/modelcontextprotocol/use-mcp/blob/main/README.md)
+  Why it matters: authoritative reference on `use-mcp README` (github.com).
+- [use-mcp React Integration](https://github.com/modelcontextprotocol/use-mcp/blob/main/src/react/README.md)
+  Why it matters: authoritative reference on `use-mcp React Integration` (github.com).
+- [Inspector Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/inspector/README.md)
+  Why it matters: authoritative reference on `Inspector Example` (github.com).
+- [Chat UI Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/chat-ui/README.md)
+  Why it matters: authoritative reference on `Chat UI Example` (github.com).
+- [Cloudflare Agents Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/cf-agents/README.md)
+  Why it matters: authoritative reference on `Cloudflare Agents Example` (github.com).
+- [Hono MCP Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/hono-mcp/README.md)
+  Why it matters: authoritative reference on `Hono MCP Example` (github.com).
+- [Integration Test Guide](https://github.com/modelcontextprotocol/use-mcp/blob/main/test/README.md)
+  Why it matters: authoritative reference on `Integration Test Guide` (github.com).
+- [Project Guidelines](https://github.com/modelcontextprotocol/use-mcp/blob/main/AGENT.md)
+  Why it matters: authoritative reference on `Project Guidelines` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: React Integration Patterns: Chat UI and Inspector](06-react-integration-patterns-chat-ui-and-inspector.md)
+- [Next Chapter: Chapter 8: Maintenance Risk, Migration, and Production Guidance](08-maintenance-risk-migration-and-production-guidance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/use-mcp-tutorial/08-maintenance-risk-migration-and-production-guidance.md b/tutorials/use-mcp-tutorial/08-maintenance-risk-migration-and-production-guidance.md
index 6058b28d..6a5ebbb4 100644
--- a/tutorials/use-mcp-tutorial/08-maintenance-risk-migration-and-production-guidance.md
+++ b/tutorials/use-mcp-tutorial/08-maintenance-risk-migration-and-production-guidance.md
@@ -7,6 +7,9 @@ parent: use-mcp Tutorial
 
 # Chapter 8: Maintenance Risk, Migration, and Production Guidance
 
+Welcome to **Chapter 8: Maintenance Risk, Migration, and Production Guidance**. In this part of **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers long-term operations for teams relying on an archived upstream package.
 
 ## Learning Goals
@@ -36,3 +39,606 @@ This chapter covers long-term operations for teams relying on an archived upstre
 You now have a pragmatic operating and migration strategy for `use-mcp` deployments.
 
 Return to the [use-mcp Tutorial index](index.md).
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- tutorial slug: **use-mcp-tutorial**
+- chapter focus: **Chapter 8: Maintenance Risk, Migration, and Production Guidance**
+- system context: **Use Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Maintenance Risk, Migration, and Production Guidance`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [use-mcp README](https://github.com/modelcontextprotocol/use-mcp/blob/main/README.md)
+- [use-mcp React Integration](https://github.com/modelcontextprotocol/use-mcp/blob/main/src/react/README.md)
+- [Inspector Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/inspector/README.md)
+- [Chat UI Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/chat-ui/README.md)
+- [Cloudflare Agents Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/cf-agents/README.md)
+- [Hono MCP Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/hono-mcp/README.md)
+- [Integration Test Guide](https://github.com/modelcontextprotocol/use-mcp/blob/main/test/README.md)
+- [Project Guidelines](https://github.com/modelcontextprotocol/use-mcp/blob/main/AGENT.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP TypeScript SDK Tutorial](../mcp-typescript-sdk-tutorial/)
+- [MCP Use Tutorial](../mcp-use-tutorial/)
+- [MCP Ext Apps Tutorial](../mcp-ext-apps-tutorial/)
+- [MCP Inspector Tutorial](../mcp-inspector-tutorial/)
+- [Chapter 1: Getting Started and Archived Context](01-getting-started-and-archived-context.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Maintenance Risk, Migration, and Production Guidance`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Maintenance Risk, Migration, and Production Guidance
+
+- tutorial context: **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Maintenance Risk, Migration, and Production Guidance` as an operating subsystem inside **use-mcp Tutorial: React Hook Patterns for MCP Client Integration**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Maintenance Risk, Migration, and Production Guidance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [use-mcp README](https://github.com/modelcontextprotocol/use-mcp/blob/main/README.md)
+  Why it matters: authoritative reference on `use-mcp README` (github.com).
+- [use-mcp React Integration](https://github.com/modelcontextprotocol/use-mcp/blob/main/src/react/README.md)
+  Why it matters: authoritative reference on `use-mcp React Integration` (github.com).
+- [Inspector Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/inspector/README.md)
+  Why it matters: authoritative reference on `Inspector Example` (github.com).
+- [Chat UI Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/chat-ui/README.md)
+  Why it matters: authoritative reference on `Chat UI Example` (github.com).
+- [Cloudflare Agents Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/cf-agents/README.md)
+  Why it matters: authoritative reference on `Cloudflare Agents Example` (github.com).
+- [Hono MCP Example](https://github.com/modelcontextprotocol/use-mcp/blob/main/examples/servers/hono-mcp/README.md)
+  Why it matters: authoritative reference on `Hono MCP Example` (github.com).
+- [Integration Test Guide](https://github.com/modelcontextprotocol/use-mcp/blob/main/test/README.md)
+  Why it matters: authoritative reference on `Integration Test Guide` (github.com).
+- [Project Guidelines](https://github.com/modelcontextprotocol/use-mcp/blob/main/AGENT.md)
+  Why it matters: authoritative reference on `Project Guidelines` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Testing, Debugging, and Integration Servers](07-testing-debugging-and-integration-servers.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vercel-ai-tutorial/01-getting-started.md b/tutorials/vercel-ai-tutorial/01-getting-started.md
index c241aba4..c4bc9747 100644
--- a/tutorials/vercel-ai-tutorial/01-getting-started.md
+++ b/tutorials/vercel-ai-tutorial/01-getting-started.md
@@ -337,3 +337,295 @@ Now that you understand Vercel AI basics, let's explore text generation in depth
 5. Add rate limiting and request throttling
 
 *What kind of AI application will you build first?* 🤖
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- tutorial slug: **vercel-ai-tutorial**
+- chapter focus: **Chapter 1: Getting Started with Vercel AI**
+- system context: **Vercel Ai Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started with Vercel AI`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [AI SDK Repository](https://github.com/vercel/ai)
+- [AI SDK Releases](https://github.com/vercel/ai/releases)
+- [AI SDK Docs](https://ai-sdk.dev)
+
+### Cross-Tutorial Connection Map
+
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [OpenAI Realtime Agents Tutorial](../openai-realtime-agents-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started with Vercel AI`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started with Vercel AI
+
+- tutorial context: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started with Vercel AI
+
+- tutorial context: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started with Vercel AI
+
+- tutorial context: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started with Vercel AI
+
+- tutorial context: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started with Vercel AI
+
+- tutorial context: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started with Vercel AI
+
+- tutorial context: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started with Vercel AI
+
+- tutorial context: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started with Vercel AI
+
+- tutorial context: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started with Vercel AI
+
+- tutorial context: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started with Vercel AI
+
+- tutorial context: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started with Vercel AI
+
+- tutorial context: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started with Vercel AI
+
+- tutorial context: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `openai`, `className`, `error` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Vercel AI` as an operating subsystem inside **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `message`, `text`, `messages` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Vercel AI` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `openai`.
+2. **Input normalization**: shape incoming data so `className` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `error`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [AI SDK Repository](https://github.com/vercel/ai)
+  Why it matters: authoritative reference on `AI SDK Repository` (github.com).
+- [AI SDK Releases](https://github.com/vercel/ai/releases)
+  Why it matters: authoritative reference on `AI SDK Releases` (github.com).
+- [AI SDK Docs](https://ai-sdk.dev)
+  Why it matters: authoritative reference on `AI SDK Docs` (ai-sdk.dev).
+
+Suggested trace strategy:
+- search upstream code for `openai` and `className` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Text Generation](02-text-generation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vercel-ai-tutorial/02-text-generation.md b/tutorials/vercel-ai-tutorial/02-text-generation.md
index 8cd5835e..a9aff7af 100644
--- a/tutorials/vercel-ai-tutorial/02-text-generation.md
+++ b/tutorials/vercel-ai-tutorial/02-text-generation.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Text Generation
 
+Welcome to **Chapter 2: Text Generation**. In this part of **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Welcome back! Now that you have Vercel AI set up and running, it's time to dive deep into text generation. Think of text generation as the heart of AI applications - it's where you transform prompts into meaningful responses, stories, code, and more.
 
 ## Understanding Text Generation
@@ -451,3 +454,188 @@ Ready to take your AI applications to the next level? In [Chapter 3: Streaming R
 5. Experiment with different temperature settings for various use cases
 
 *What's the most interesting text generation application you can think of?* 🤖
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- tutorial slug: **vercel-ai-tutorial**
+- chapter focus: **Chapter 2: Text Generation**
+- system context: **Vercel Ai Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Text Generation`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [AI SDK Repository](https://github.com/vercel/ai)
+- [AI SDK Releases](https://github.com/vercel/ai/releases)
+- [AI SDK Docs](https://ai-sdk.dev)
+
+### Cross-Tutorial Connection Map
+
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [OpenAI Realtime Agents Tutorial](../openai-realtime-agents-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Text Generation`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Text Generation
+
+- tutorial context: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Text Generation
+
+- tutorial context: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Text Generation
+
+- tutorial context: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `text`, `prompt`, `temperature` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Text Generation` as an operating subsystem inside **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `model`, `models`, `className` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Text Generation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `text`.
+2. **Input normalization**: shape incoming data so `prompt` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `temperature`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [AI SDK Repository](https://github.com/vercel/ai)
+  Why it matters: authoritative reference on `AI SDK Repository` (github.com).
+- [AI SDK Releases](https://github.com/vercel/ai/releases)
+  Why it matters: authoritative reference on `AI SDK Releases` (github.com).
+- [AI SDK Docs](https://ai-sdk.dev)
+  Why it matters: authoritative reference on `AI SDK Docs` (ai-sdk.dev).
+
+Suggested trace strategy:
+- search upstream code for `text` and `prompt` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Vercel AI](01-getting-started.md)
+- [Next Chapter: Chapter 3: Streaming Responses](03-streaming-responses.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vercel-ai-tutorial/03-streaming-responses.md b/tutorials/vercel-ai-tutorial/03-streaming-responses.md
index 21398507..bd2fb0d1 100644
--- a/tutorials/vercel-ai-tutorial/03-streaming-responses.md
+++ b/tutorials/vercel-ai-tutorial/03-streaming-responses.md
@@ -573,3 +573,152 @@ Ready for more advanced AI capabilities? In [Chapter 4: Function Calling](04-fun
 5. Build a collaborative streaming chat with multiple users
 
 *How will you use streaming to enhance your AI applications?* ⚡
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**
+- tutorial slug: **vercel-ai-tutorial**
+- chapter focus: **Chapter 3: Streaming Responses**
+- system context: **Vercel Ai Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Streaming Responses`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [AI SDK Repository](https://github.com/vercel/ai)
+- [AI SDK Releases](https://github.com/vercel/ai/releases)
+- [AI SDK Docs](https://ai-sdk.dev)
+
+### Cross-Tutorial Connection Map
+
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [OpenAI Realtime Agents Tutorial](../openai-realtime-agents-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Streaming Responses`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `className`, `messages`, `text` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Streaming Responses` as an operating subsystem inside **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `stream`, `flex`, `gray` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Streaming Responses` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `className`.
+2. **Input normalization**: shape incoming data so `messages` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `text`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [AI SDK Repository](https://github.com/vercel/ai)
+  Why it matters: authoritative reference on `AI SDK Repository` (github.com).
+- [AI SDK Releases](https://github.com/vercel/ai/releases)
+  Why it matters: authoritative reference on `AI SDK Releases` (github.com).
+- [AI SDK Docs](https://ai-sdk.dev)
+  Why it matters: authoritative reference on `AI SDK Docs` (ai-sdk.dev).
+
+Suggested trace strategy:
+- search upstream code for `className` and `messages` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Text Generation](02-text-generation.md)
+- [Next Chapter: Chapter 4: Function Calling](04-function-calling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vercel-ai-tutorial/04-function-calling.md b/tutorials/vercel-ai-tutorial/04-function-calling.md
index 8f9bfc46..821f4838 100644
--- a/tutorials/vercel-ai-tutorial/04-function-calling.md
+++ b/tutorials/vercel-ai-tutorial/04-function-calling.md
@@ -589,3 +589,53 @@ Ready to generate structured data with type safety? In [Chapter 5: Structured Ou
 5. Implement tool versioning and rollback capabilities
 
 *What powerful tools will you create for your AI assistant?* 🛠️
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `query`, `tool`, `error` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Function Calling` as an operating subsystem inside **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `className`, `tools`, `call` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Function Calling` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `query`.
+2. **Input normalization**: shape incoming data so `tool` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `error`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [AI SDK Repository](https://github.com/vercel/ai)
+  Why it matters: authoritative reference on `AI SDK Repository` (github.com).
+- [AI SDK Releases](https://github.com/vercel/ai/releases)
+  Why it matters: authoritative reference on `AI SDK Releases` (github.com).
+- [AI SDK Docs](https://ai-sdk.dev)
+  Why it matters: authoritative reference on `AI SDK Docs` (ai-sdk.dev).
+
+Suggested trace strategy:
+- search upstream code for `query` and `tool` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Streaming Responses](03-streaming-responses.md)
+- [Next Chapter: Chapter 5: Structured Outputs](05-structured-outputs.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vercel-ai-tutorial/05-structured-outputs.md b/tutorials/vercel-ai-tutorial/05-structured-outputs.md
index 1677c279..be208c97 100644
--- a/tutorials/vercel-ai-tutorial/05-structured-outputs.md
+++ b/tutorials/vercel-ai-tutorial/05-structured-outputs.md
@@ -678,3 +678,53 @@ Ready to integrate AI into React applications? In [Chapter 6: React Integration]
 5. Add schema auto-completion and suggestions
 
 *What structured data will you generate with AI?* 📊
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `object`, `error`, `schema` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Structured Outputs` as an operating subsystem inside **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `className`, `name`, `array` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Structured Outputs` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `object`.
+2. **Input normalization**: shape incoming data so `error` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `schema`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [AI SDK Repository](https://github.com/vercel/ai)
+  Why it matters: authoritative reference on `AI SDK Repository` (github.com).
+- [AI SDK Releases](https://github.com/vercel/ai/releases)
+  Why it matters: authoritative reference on `AI SDK Releases` (github.com).
+- [AI SDK Docs](https://ai-sdk.dev)
+  Why it matters: authoritative reference on `AI SDK Docs` (ai-sdk.dev).
+
+Suggested trace strategy:
+- search upstream code for `object` and `error` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Function Calling](04-function-calling.md)
+- [Next Chapter: Chapter 6: React Integration](06-react-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vercel-ai-tutorial/06-react-integration.md b/tutorials/vercel-ai-tutorial/06-react-integration.md
index 59dde61d..3b7505a7 100644
--- a/tutorials/vercel-ai-tutorial/06-react-integration.md
+++ b/tutorials/vercel-ai-tutorial/06-react-integration.md
@@ -885,3 +885,53 @@ Ready to build full-stack AI applications? In [Chapter 7: Next.js Applications](
 5. Create an AI-powered data visualization component
 
 *What AI-powered React component will you build next?* ⚛️
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `className`, `text`, `error` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: React Integration` as an operating subsystem inside **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `rounded`, `button`, `prompt` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: React Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `className`.
+2. **Input normalization**: shape incoming data so `text` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `error`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [AI SDK Repository](https://github.com/vercel/ai)
+  Why it matters: authoritative reference on `AI SDK Repository` (github.com).
+- [AI SDK Releases](https://github.com/vercel/ai/releases)
+  Why it matters: authoritative reference on `AI SDK Releases` (github.com).
+- [AI SDK Docs](https://ai-sdk.dev)
+  Why it matters: authoritative reference on `AI SDK Docs` (ai-sdk.dev).
+
+Suggested trace strategy:
+- search upstream code for `className` and `text` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Structured Outputs](05-structured-outputs.md)
+- [Next Chapter: Chapter 7: Next.js Applications](07-nextjs-applications.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vercel-ai-tutorial/07-nextjs-applications.md b/tutorials/vercel-ai-tutorial/07-nextjs-applications.md
index 99796725..dd104142 100644
--- a/tutorials/vercel-ai-tutorial/07-nextjs-applications.md
+++ b/tutorials/vercel-ai-tutorial/07-nextjs-applications.md
@@ -1006,3 +1006,53 @@ Ready for production deployment? In [Chapter 8: Production Deployment](08-produc
 5. Implement AI model versioning and A/B testing
 
 *What full-stack AI application will you build next?* 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `className`, `text`, `error` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Next.js Applications` as an operating subsystem inside **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `post`, `gray`, `user` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Next.js Applications` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `className`.
+2. **Input normalization**: shape incoming data so `text` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `error`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [AI SDK Repository](https://github.com/vercel/ai)
+  Why it matters: authoritative reference on `AI SDK Repository` (github.com).
+- [AI SDK Releases](https://github.com/vercel/ai/releases)
+  Why it matters: authoritative reference on `AI SDK Releases` (github.com).
+- [AI SDK Docs](https://ai-sdk.dev)
+  Why it matters: authoritative reference on `AI SDK Docs` (ai-sdk.dev).
+
+Suggested trace strategy:
+- search upstream code for `className` and `text` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: React Integration](06-react-integration.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vercel-ai-tutorial/08-production-deployment.md b/tutorials/vercel-ai-tutorial/08-production-deployment.md
index 5c4e246b..7719cf5b 100644
--- a/tutorials/vercel-ai-tutorial/08-production-deployment.md
+++ b/tutorials/vercel-ai-tutorial/08-production-deployment.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Congratulations! 🎉 You've made it to the final chapter. Now it's time to take your AI applications from development to production. We'll deploy to Vercel, implement monitoring, optimize performance, and ensure your applications can handle real-world traffic.
 
 ## Vercel Deployment Setup
@@ -893,3 +896,52 @@ You've accomplished something amazing! The world of AI application development i
 ---
 
 *Thank you for completing the Vercel AI Tutorial! Your journey into AI-powered application development has just begun.* 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `error`, `model`, `static` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **Vercel AI SDK Tutorial: Production TypeScript AI Apps and Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `NextResponse`, `userId`, `Promise` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `error`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `static`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [AI SDK Repository](https://github.com/vercel/ai)
+  Why it matters: authoritative reference on `AI SDK Repository` (github.com).
+- [AI SDK Releases](https://github.com/vercel/ai/releases)
+  Why it matters: authoritative reference on `AI SDK Releases` (github.com).
+- [AI SDK Docs](https://ai-sdk.dev)
+  Why it matters: authoritative reference on `AI SDK Docs` (ai-sdk.dev).
+
+Suggested trace strategy:
+- search upstream code for `error` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Next.js Applications](07-nextjs-applications.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vibe-kanban-tutorial/01-getting-started.md b/tutorials/vibe-kanban-tutorial/01-getting-started.md
index 50f72bd5..ed71a07f 100644
--- a/tutorials/vibe-kanban-tutorial/01-getting-started.md
+++ b/tutorials/vibe-kanban-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Vibe Kanban Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Vibe Kanban running with your preferred coding agent environment.
 
 ## Learning Goals
@@ -48,3 +51,586 @@ npx vibe-kanban
 You now have Vibe Kanban up and ready for multi-agent task orchestration.
 
 Next: [Chapter 2: Orchestration Architecture](02-orchestration-architecture.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- tutorial slug: **vibe-kanban-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Vibe Kanban Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Vibe Kanban Repository](https://github.com/BloopAI/vibe-kanban)
+- [Vibe Kanban Docs](https://vibekanban.com/docs)
+- [Vibe Kanban Self-Hosting](https://vibekanban.com/docs/self-hosting)
+- [Vibe Kanban README](https://github.com/BloopAI/vibe-kanban/blob/main/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Superset Terminal Tutorial](../superset-terminal-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Goose Tutorial](../goose-tutorial/)
+- [Opcode Tutorial](../opcode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `vibe`, `kanban` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `vibe`.
+2. **Input normalization**: shape incoming data so `kanban` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Vibe Kanban Repository](https://github.com/BloopAI/vibe-kanban)
+  Why it matters: authoritative reference on `Vibe Kanban Repository` (github.com).
+- [Vibe Kanban Docs](https://vibekanban.com/docs)
+  Why it matters: authoritative reference on `Vibe Kanban Docs` (vibekanban.com).
+- [Vibe Kanban Self-Hosting](https://vibekanban.com/docs/self-hosting)
+  Why it matters: authoritative reference on `Vibe Kanban Self-Hosting` (vibekanban.com).
+- [Vibe Kanban README](https://github.com/BloopAI/vibe-kanban/blob/main/README.md)
+  Why it matters: authoritative reference on `Vibe Kanban README` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `vibe` and `kanban` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Orchestration Architecture](02-orchestration-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vibe-kanban-tutorial/02-orchestration-architecture.md b/tutorials/vibe-kanban-tutorial/02-orchestration-architecture.md
index 3ca621a1..321c6d25 100644
--- a/tutorials/vibe-kanban-tutorial/02-orchestration-architecture.md
+++ b/tutorials/vibe-kanban-tutorial/02-orchestration-architecture.md
@@ -7,6 +7,9 @@ parent: Vibe Kanban Tutorial
 
 # Chapter 2: Orchestration Architecture
 
+Welcome to **Chapter 2: Orchestration Architecture**. In this part of **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains the core architecture that turns Vibe Kanban into a multi-agent command center.
 
 ## Learning Goals
@@ -39,3 +42,595 @@ Vibe Kanban helps teams avoid context fragmentation by keeping planning, executi
 You now understand how Vibe Kanban coordinates planning and execution across many coding agents.
 
 Next: [Chapter 3: Multi-Agent Execution Strategies](03-multi-agent-execution-strategies.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- tutorial slug: **vibe-kanban-tutorial**
+- chapter focus: **Chapter 2: Orchestration Architecture**
+- system context: **Vibe Kanban Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Orchestration Architecture`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Vibe Kanban Repository](https://github.com/BloopAI/vibe-kanban)
+- [Vibe Kanban Docs](https://vibekanban.com/docs)
+- [Vibe Kanban Self-Hosting](https://vibekanban.com/docs/self-hosting)
+- [Vibe Kanban README](https://github.com/BloopAI/vibe-kanban/blob/main/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Superset Terminal Tutorial](../superset-terminal-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Goose Tutorial](../goose-tutorial/)
+- [Opcode Tutorial](../opcode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Orchestration Architecture`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Orchestration Architecture
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Orchestration Architecture` as an operating subsystem inside **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Orchestration Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Vibe Kanban Repository](https://github.com/BloopAI/vibe-kanban)
+  Why it matters: authoritative reference on `Vibe Kanban Repository` (github.com).
+- [Vibe Kanban Docs](https://vibekanban.com/docs)
+  Why it matters: authoritative reference on `Vibe Kanban Docs` (vibekanban.com).
+- [Vibe Kanban Self-Hosting](https://vibekanban.com/docs/self-hosting)
+  Why it matters: authoritative reference on `Vibe Kanban Self-Hosting` (vibekanban.com).
+- [Vibe Kanban README](https://github.com/BloopAI/vibe-kanban/blob/main/README.md)
+  Why it matters: authoritative reference on `Vibe Kanban README` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Multi-Agent Execution Strategies](03-multi-agent-execution-strategies.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vibe-kanban-tutorial/03-multi-agent-execution-strategies.md b/tutorials/vibe-kanban-tutorial/03-multi-agent-execution-strategies.md
index 7003c789..b3fa9937 100644
--- a/tutorials/vibe-kanban-tutorial/03-multi-agent-execution-strategies.md
+++ b/tutorials/vibe-kanban-tutorial/03-multi-agent-execution-strategies.md
@@ -7,6 +7,9 @@ parent: Vibe Kanban Tutorial
 
 # Chapter 3: Multi-Agent Execution Strategies
 
+Welcome to **Chapter 3: Multi-Agent Execution Strategies**. In this part of **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on execution patterns that maximize throughput while protecting quality.
 
 ## Learning Goals
@@ -40,3 +43,595 @@ This chapter focuses on execution patterns that maximize throughput while protec
 You now can structure multi-agent execution for both speed and reliability.
 
 Next: [Chapter 4: MCP and Configuration Control](04-mcp-and-configuration-control.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- tutorial slug: **vibe-kanban-tutorial**
+- chapter focus: **Chapter 3: Multi-Agent Execution Strategies**
+- system context: **Vibe Kanban Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Multi-Agent Execution Strategies`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Vibe Kanban Repository](https://github.com/BloopAI/vibe-kanban)
+- [Vibe Kanban Docs](https://vibekanban.com/docs)
+- [Vibe Kanban Self-Hosting](https://vibekanban.com/docs/self-hosting)
+- [Vibe Kanban README](https://github.com/BloopAI/vibe-kanban/blob/main/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Superset Terminal Tutorial](../superset-terminal-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Goose Tutorial](../goose-tutorial/)
+- [Opcode Tutorial](../opcode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Multi-Agent Execution Strategies`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Multi-Agent Execution Strategies
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Multi-Agent Execution Strategies` as an operating subsystem inside **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Multi-Agent Execution Strategies` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Vibe Kanban Repository](https://github.com/BloopAI/vibe-kanban)
+  Why it matters: authoritative reference on `Vibe Kanban Repository` (github.com).
+- [Vibe Kanban Docs](https://vibekanban.com/docs)
+  Why it matters: authoritative reference on `Vibe Kanban Docs` (vibekanban.com).
+- [Vibe Kanban Self-Hosting](https://vibekanban.com/docs/self-hosting)
+  Why it matters: authoritative reference on `Vibe Kanban Self-Hosting` (vibekanban.com).
+- [Vibe Kanban README](https://github.com/BloopAI/vibe-kanban/blob/main/README.md)
+  Why it matters: authoritative reference on `Vibe Kanban README` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Orchestration Architecture](02-orchestration-architecture.md)
+- [Next Chapter: Chapter 4: MCP and Configuration Control](04-mcp-and-configuration-control.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vibe-kanban-tutorial/04-mcp-and-configuration-control.md b/tutorials/vibe-kanban-tutorial/04-mcp-and-configuration-control.md
index 5b4376bf..dbe1a910 100644
--- a/tutorials/vibe-kanban-tutorial/04-mcp-and-configuration-control.md
+++ b/tutorials/vibe-kanban-tutorial/04-mcp-and-configuration-control.md
@@ -7,6 +7,9 @@ parent: Vibe Kanban Tutorial
 
 # Chapter 4: MCP and Configuration Control
 
+Welcome to **Chapter 4: MCP and Configuration Control**. In this part of **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers how Vibe Kanban centralizes MCP and runtime configuration to reduce agent drift.
 
 ## Learning Goals
@@ -41,3 +44,595 @@ This chapter covers how Vibe Kanban centralizes MCP and runtime configuration to
 You now have a practical model for MCP/runtime configuration governance in Vibe Kanban.
 
 Next: [Chapter 5: Review and Quality Gates](05-review-and-quality-gates.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- tutorial slug: **vibe-kanban-tutorial**
+- chapter focus: **Chapter 4: MCP and Configuration Control**
+- system context: **Vibe Kanban Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: MCP and Configuration Control`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Vibe Kanban Repository](https://github.com/BloopAI/vibe-kanban)
+- [Vibe Kanban Docs](https://vibekanban.com/docs)
+- [Vibe Kanban Self-Hosting](https://vibekanban.com/docs/self-hosting)
+- [Vibe Kanban README](https://github.com/BloopAI/vibe-kanban/blob/main/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Superset Terminal Tutorial](../superset-terminal-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Goose Tutorial](../goose-tutorial/)
+- [Opcode Tutorial](../opcode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: MCP and Configuration Control`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: MCP and Configuration Control
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: MCP and Configuration Control` as an operating subsystem inside **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: MCP and Configuration Control` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Vibe Kanban Repository](https://github.com/BloopAI/vibe-kanban)
+  Why it matters: authoritative reference on `Vibe Kanban Repository` (github.com).
+- [Vibe Kanban Docs](https://vibekanban.com/docs)
+  Why it matters: authoritative reference on `Vibe Kanban Docs` (vibekanban.com).
+- [Vibe Kanban Self-Hosting](https://vibekanban.com/docs/self-hosting)
+  Why it matters: authoritative reference on `Vibe Kanban Self-Hosting` (vibekanban.com).
+- [Vibe Kanban README](https://github.com/BloopAI/vibe-kanban/blob/main/README.md)
+  Why it matters: authoritative reference on `Vibe Kanban README` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Multi-Agent Execution Strategies](03-multi-agent-execution-strategies.md)
+- [Next Chapter: Chapter 5: Review and Quality Gates](05-review-and-quality-gates.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vibe-kanban-tutorial/05-review-and-quality-gates.md b/tutorials/vibe-kanban-tutorial/05-review-and-quality-gates.md
index de1ddab5..894458aa 100644
--- a/tutorials/vibe-kanban-tutorial/05-review-and-quality-gates.md
+++ b/tutorials/vibe-kanban-tutorial/05-review-and-quality-gates.md
@@ -7,6 +7,9 @@ parent: Vibe Kanban Tutorial
 
 # Chapter 5: Review and Quality Gates
 
+Welcome to **Chapter 5: Review and Quality Gates**. In this part of **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter defines the human-in-the-loop controls that keep multi-agent output production-ready.
 
 ## Learning Goals
@@ -40,3 +43,595 @@ This chapter defines the human-in-the-loop controls that keep multi-agent output
 You now have a high-throughput review model for multi-agent task output.
 
 Next: [Chapter 6: Remote Access and Self-Hosting](06-remote-access-and-self-hosting.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- tutorial slug: **vibe-kanban-tutorial**
+- chapter focus: **Chapter 5: Review and Quality Gates**
+- system context: **Vibe Kanban Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Review and Quality Gates`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Vibe Kanban Repository](https://github.com/BloopAI/vibe-kanban)
+- [Vibe Kanban Docs](https://vibekanban.com/docs)
+- [Vibe Kanban Self-Hosting](https://vibekanban.com/docs/self-hosting)
+- [Vibe Kanban README](https://github.com/BloopAI/vibe-kanban/blob/main/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Superset Terminal Tutorial](../superset-terminal-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Goose Tutorial](../goose-tutorial/)
+- [Opcode Tutorial](../opcode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Review and Quality Gates`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Review and Quality Gates
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Review and Quality Gates` as an operating subsystem inside **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Review and Quality Gates` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Vibe Kanban Repository](https://github.com/BloopAI/vibe-kanban)
+  Why it matters: authoritative reference on `Vibe Kanban Repository` (github.com).
+- [Vibe Kanban Docs](https://vibekanban.com/docs)
+  Why it matters: authoritative reference on `Vibe Kanban Docs` (vibekanban.com).
+- [Vibe Kanban Self-Hosting](https://vibekanban.com/docs/self-hosting)
+  Why it matters: authoritative reference on `Vibe Kanban Self-Hosting` (vibekanban.com).
+- [Vibe Kanban README](https://github.com/BloopAI/vibe-kanban/blob/main/README.md)
+  Why it matters: authoritative reference on `Vibe Kanban README` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: MCP and Configuration Control](04-mcp-and-configuration-control.md)
+- [Next Chapter: Chapter 6: Remote Access and Self-Hosting](06-remote-access-and-self-hosting.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vibe-kanban-tutorial/06-remote-access-and-self-hosting.md b/tutorials/vibe-kanban-tutorial/06-remote-access-and-self-hosting.md
index 7510e088..8916ecdf 100644
--- a/tutorials/vibe-kanban-tutorial/06-remote-access-and-self-hosting.md
+++ b/tutorials/vibe-kanban-tutorial/06-remote-access-and-self-hosting.md
@@ -7,6 +7,9 @@ parent: Vibe Kanban Tutorial
 
 # Chapter 6: Remote Access and Self-Hosting
 
+Welcome to **Chapter 6: Remote Access and Self-Hosting**. In this part of **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers remote deployment patterns, editor integration, and secure remote operations.
 
 ## Learning Goals
@@ -35,3 +38,595 @@ This chapter covers remote deployment patterns, editor integration, and secure r
 You now know how to run Vibe Kanban beyond a single local machine safely.
 
 Next: [Chapter 7: Development and Source Build Workflow](07-development-and-source-build-workflow.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- tutorial slug: **vibe-kanban-tutorial**
+- chapter focus: **Chapter 6: Remote Access and Self-Hosting**
+- system context: **Vibe Kanban Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Remote Access and Self-Hosting`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Vibe Kanban Repository](https://github.com/BloopAI/vibe-kanban)
+- [Vibe Kanban Docs](https://vibekanban.com/docs)
+- [Vibe Kanban Self-Hosting](https://vibekanban.com/docs/self-hosting)
+- [Vibe Kanban README](https://github.com/BloopAI/vibe-kanban/blob/main/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Superset Terminal Tutorial](../superset-terminal-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Goose Tutorial](../goose-tutorial/)
+- [Opcode Tutorial](../opcode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Remote Access and Self-Hosting`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Remote Access and Self-Hosting
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Remote Access and Self-Hosting` as an operating subsystem inside **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Remote Access and Self-Hosting` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Vibe Kanban Repository](https://github.com/BloopAI/vibe-kanban)
+  Why it matters: authoritative reference on `Vibe Kanban Repository` (github.com).
+- [Vibe Kanban Docs](https://vibekanban.com/docs)
+  Why it matters: authoritative reference on `Vibe Kanban Docs` (vibekanban.com).
+- [Vibe Kanban Self-Hosting](https://vibekanban.com/docs/self-hosting)
+  Why it matters: authoritative reference on `Vibe Kanban Self-Hosting` (vibekanban.com).
+- [Vibe Kanban README](https://github.com/BloopAI/vibe-kanban/blob/main/README.md)
+  Why it matters: authoritative reference on `Vibe Kanban README` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Review and Quality Gates](05-review-and-quality-gates.md)
+- [Next Chapter: Chapter 7: Development and Source Build Workflow](07-development-and-source-build-workflow.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vibe-kanban-tutorial/07-development-and-source-build-workflow.md b/tutorials/vibe-kanban-tutorial/07-development-and-source-build-workflow.md
index 6a50fc6a..e5b58df3 100644
--- a/tutorials/vibe-kanban-tutorial/07-development-and-source-build-workflow.md
+++ b/tutorials/vibe-kanban-tutorial/07-development-and-source-build-workflow.md
@@ -7,6 +7,9 @@ parent: Vibe Kanban Tutorial
 
 # Chapter 7: Development and Source Build Workflow
 
+Welcome to **Chapter 7: Development and Source Build Workflow**. In this part of **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter targets contributors building and extending Vibe Kanban from source.
 
 ## Learning Goals
@@ -47,3 +50,587 @@ pnpm run dev
 You now have a contributor-ready workflow for iterating on Vibe Kanban itself.
 
 Next: [Chapter 8: Production Operations and Governance](08-production-operations-and-governance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- tutorial slug: **vibe-kanban-tutorial**
+- chapter focus: **Chapter 7: Development and Source Build Workflow**
+- system context: **Vibe Kanban Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Development and Source Build Workflow`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Vibe Kanban Repository](https://github.com/BloopAI/vibe-kanban)
+- [Vibe Kanban Docs](https://vibekanban.com/docs)
+- [Vibe Kanban Self-Hosting](https://vibekanban.com/docs/self-hosting)
+- [Vibe Kanban README](https://github.com/BloopAI/vibe-kanban/blob/main/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Superset Terminal Tutorial](../superset-terminal-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Goose Tutorial](../goose-tutorial/)
+- [Opcode Tutorial](../opcode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Development and Source Build Workflow`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Development and Source Build Workflow
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `pnpm` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Development and Source Build Workflow` as an operating subsystem inside **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Development and Source Build Workflow` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `pnpm`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Vibe Kanban Repository](https://github.com/BloopAI/vibe-kanban)
+  Why it matters: authoritative reference on `Vibe Kanban Repository` (github.com).
+- [Vibe Kanban Docs](https://vibekanban.com/docs)
+  Why it matters: authoritative reference on `Vibe Kanban Docs` (vibekanban.com).
+- [Vibe Kanban Self-Hosting](https://vibekanban.com/docs/self-hosting)
+  Why it matters: authoritative reference on `Vibe Kanban Self-Hosting` (vibekanban.com).
+- [Vibe Kanban README](https://github.com/BloopAI/vibe-kanban/blob/main/README.md)
+  Why it matters: authoritative reference on `Vibe Kanban README` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `pnpm` and `pnpm` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Remote Access and Self-Hosting](06-remote-access-and-self-hosting.md)
+- [Next Chapter: Chapter 8: Production Operations and Governance](08-production-operations-and-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vibe-kanban-tutorial/08-production-operations-and-governance.md b/tutorials/vibe-kanban-tutorial/08-production-operations-and-governance.md
index 953be74f..f84dec83 100644
--- a/tutorials/vibe-kanban-tutorial/08-production-operations-and-governance.md
+++ b/tutorials/vibe-kanban-tutorial/08-production-operations-and-governance.md
@@ -7,6 +7,9 @@ parent: Vibe Kanban Tutorial
 
 # Chapter 8: Production Operations and Governance
 
+Welcome to **Chapter 8: Production Operations and Governance**. In this part of **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter defines operational and governance practices for production Vibe Kanban deployments.
 
 ## Learning Goals
@@ -37,3 +40,594 @@ This chapter defines operational and governance practices for production Vibe Ka
 You now have a full operational runbook for managing coding-agent orchestration with Vibe Kanban.
 
 Continue with the [Opcode Tutorial](../opcode-tutorial/) for GUI-native Claude Code workflows.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- tutorial slug: **vibe-kanban-tutorial**
+- chapter focus: **Chapter 8: Production Operations and Governance**
+- system context: **Vibe Kanban Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Operations and Governance`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Vibe Kanban Repository](https://github.com/BloopAI/vibe-kanban)
+- [Vibe Kanban Docs](https://vibekanban.com/docs)
+- [Vibe Kanban Self-Hosting](https://vibekanban.com/docs/self-hosting)
+- [Vibe Kanban README](https://github.com/BloopAI/vibe-kanban/blob/main/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Superset Terminal Tutorial](../superset-terminal-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Goose Tutorial](../goose-tutorial/)
+- [Opcode Tutorial](../opcode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Operations and Governance`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Operations and Governance` as an operating subsystem inside **Vibe Kanban Tutorial: Multi-Agent Orchestration Board for Coding Workflows**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Operations and Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Vibe Kanban Repository](https://github.com/BloopAI/vibe-kanban)
+  Why it matters: authoritative reference on `Vibe Kanban Repository` (github.com).
+- [Vibe Kanban Docs](https://vibekanban.com/docs)
+  Why it matters: authoritative reference on `Vibe Kanban Docs` (vibekanban.com).
+- [Vibe Kanban Self-Hosting](https://vibekanban.com/docs/self-hosting)
+  Why it matters: authoritative reference on `Vibe Kanban Self-Hosting` (vibekanban.com).
+- [Vibe Kanban README](https://github.com/BloopAI/vibe-kanban/blob/main/README.md)
+  Why it matters: authoritative reference on `Vibe Kanban README` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Development and Source Build Workflow](07-development-and-source-build-workflow.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vibesdk-tutorial/01-getting-started-and-deployment-paths.md b/tutorials/vibesdk-tutorial/01-getting-started-and-deployment-paths.md
index d32cfeba..62187fe9 100644
--- a/tutorials/vibesdk-tutorial/01-getting-started-and-deployment-paths.md
+++ b/tutorials/vibesdk-tutorial/01-getting-started-and-deployment-paths.md
@@ -7,6 +7,9 @@ parent: VibeSDK Tutorial
 
 # Chapter 1: Getting Started and Deployment Paths
 
+Welcome to **Chapter 1: Getting Started and Deployment Paths**. In this part of **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets `cloudflare/vibesdk` running in a local development loop, then maps the path to production-style deployment.
 
 ## Learning Goals
@@ -102,3 +105,541 @@ After startup, validate all four before moving on:
 You now have a practical bootstrap playbook for VibeSDK and a clear path from local development to managed deployment.
 
 Next: [Chapter 2: System Architecture](02-system-architecture.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- tutorial slug: **vibesdk-tutorial**
+- chapter focus: **Chapter 1: Getting Started and Deployment Paths**
+- system context: **Vibesdk Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started and Deployment Paths`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [VibeSDK Repository](https://github.com/cloudflare/vibesdk)
+- [VibeSDK Releases](https://github.com/cloudflare/vibesdk/releases)
+- [VibeSDK Setup Guide](https://github.com/cloudflare/vibesdk/blob/main/docs/setup.md)
+- [VibeSDK SDK Documentation](https://github.com/cloudflare/vibesdk/blob/main/sdk/README.md)
+- [Live Demo](https://build.cloudflare.dev/)
+
+### Cross-Tutorial Connection Map
+
+- [bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Vercel AI Tutorial](../vercel-ai-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [Chapter 1: Getting Started and Deployment Paths](01-getting-started-and-deployment-paths.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started and Deployment Paths`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started and Deployment Paths
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `install`, `setup`, `migrate` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started and Deployment Paths` as an operating subsystem inside **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `local` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started and Deployment Paths` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `install`.
+2. **Input normalization**: shape incoming data so `setup` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `migrate`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [VibeSDK Repository](https://github.com/cloudflare/vibesdk)
+  Why it matters: authoritative reference on `VibeSDK Repository` (github.com).
+- [VibeSDK Releases](https://github.com/cloudflare/vibesdk/releases)
+  Why it matters: authoritative reference on `VibeSDK Releases` (github.com).
+- [VibeSDK Setup Guide](https://github.com/cloudflare/vibesdk/blob/main/docs/setup.md)
+  Why it matters: authoritative reference on `VibeSDK Setup Guide` (github.com).
+- [VibeSDK SDK Documentation](https://github.com/cloudflare/vibesdk/blob/main/sdk/README.md)
+  Why it matters: authoritative reference on `VibeSDK SDK Documentation` (github.com).
+- [Live Demo](https://build.cloudflare.dev/)
+  Why it matters: authoritative reference on `Live Demo` (build.cloudflare.dev).
+
+Suggested trace strategy:
+- search upstream code for `install` and `setup` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: System Architecture](02-system-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vibesdk-tutorial/02-system-architecture.md b/tutorials/vibesdk-tutorial/02-system-architecture.md
index 83c61005..f070ada6 100644
--- a/tutorials/vibesdk-tutorial/02-system-architecture.md
+++ b/tutorials/vibesdk-tutorial/02-system-architecture.md
@@ -7,6 +7,9 @@ parent: VibeSDK Tutorial
 
 # Chapter 2: System Architecture
 
+Welcome to **Chapter 2: System Architecture**. In this part of **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 VibeSDK combines a React frontend, Worker API plane, Durable Object orchestration, and Cloudflare-managed infrastructure into one app-generation platform.
 
 ## Learning Goals
@@ -91,3 +94,554 @@ Before extending the platform, verify:
 You now have a clear system map for VibeSDK and can reason about where to implement changes without cross-layer confusion.
 
 Next: [Chapter 3: AI Pipeline and Phase Engine](03-ai-pipeline-and-phase-engine.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- tutorial slug: **vibesdk-tutorial**
+- chapter focus: **Chapter 2: System Architecture**
+- system context: **Vibesdk Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: System Architecture`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [VibeSDK Repository](https://github.com/cloudflare/vibesdk)
+- [VibeSDK Releases](https://github.com/cloudflare/vibesdk/releases)
+- [VibeSDK Setup Guide](https://github.com/cloudflare/vibesdk/blob/main/docs/setup.md)
+- [VibeSDK SDK Documentation](https://github.com/cloudflare/vibesdk/blob/main/sdk/README.md)
+- [Live Demo](https://build.cloudflare.dev/)
+
+### Cross-Tutorial Connection Map
+
+- [bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Vercel AI Tutorial](../vercel-ai-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [Chapter 1: Getting Started and Deployment Paths](01-getting-started-and-deployment-paths.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: System Architecture`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: System Architecture
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `graph`, `User`, `React` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: System Architecture` as an operating subsystem inside **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Vite`, `Frontend`, `Cloudflare` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: System Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `graph`.
+2. **Input normalization**: shape incoming data so `User` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `React`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [VibeSDK Repository](https://github.com/cloudflare/vibesdk)
+  Why it matters: authoritative reference on `VibeSDK Repository` (github.com).
+- [VibeSDK Releases](https://github.com/cloudflare/vibesdk/releases)
+  Why it matters: authoritative reference on `VibeSDK Releases` (github.com).
+- [VibeSDK Setup Guide](https://github.com/cloudflare/vibesdk/blob/main/docs/setup.md)
+  Why it matters: authoritative reference on `VibeSDK Setup Guide` (github.com).
+- [VibeSDK SDK Documentation](https://github.com/cloudflare/vibesdk/blob/main/sdk/README.md)
+  Why it matters: authoritative reference on `VibeSDK SDK Documentation` (github.com).
+- [Live Demo](https://build.cloudflare.dev/)
+  Why it matters: authoritative reference on `Live Demo` (build.cloudflare.dev).
+
+Suggested trace strategy:
+- search upstream code for `graph` and `User` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started and Deployment Paths](01-getting-started-and-deployment-paths.md)
+- [Next Chapter: Chapter 3: AI Pipeline and Phase Engine](03-ai-pipeline-and-phase-engine.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vibesdk-tutorial/03-ai-pipeline-and-phase-engine.md b/tutorials/vibesdk-tutorial/03-ai-pipeline-and-phase-engine.md
index a8e91b34..88c2d1d9 100644
--- a/tutorials/vibesdk-tutorial/03-ai-pipeline-and-phase-engine.md
+++ b/tutorials/vibesdk-tutorial/03-ai-pipeline-and-phase-engine.md
@@ -7,6 +7,9 @@ parent: VibeSDK Tutorial
 
 # Chapter 3: AI Pipeline and Phase Engine
 
+Welcome to **Chapter 3: AI Pipeline and Phase Engine**. In this part of **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 VibeSDK uses a structured phase engine so generation is auditable, recoverable, and tunable instead of a one-shot black box.
 
 ## Learning Goals
@@ -87,3 +90,554 @@ If generation quality regresses:
 You now understand how VibeSDK decomposes app generation into controllable phases and where to tune for reliability.
 
 Next: [Chapter 4: Sandbox and Preview Runtime](04-sandbox-and-preview-runtime.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- tutorial slug: **vibesdk-tutorial**
+- chapter focus: **Chapter 3: AI Pipeline and Phase Engine**
+- system context: **Vibesdk Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: AI Pipeline and Phase Engine`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [VibeSDK Repository](https://github.com/cloudflare/vibesdk)
+- [VibeSDK Releases](https://github.com/cloudflare/vibesdk/releases)
+- [VibeSDK Setup Guide](https://github.com/cloudflare/vibesdk/blob/main/docs/setup.md)
+- [VibeSDK SDK Documentation](https://github.com/cloudflare/vibesdk/blob/main/sdk/README.md)
+- [Live Demo](https://build.cloudflare.dev/)
+
+### Cross-Tutorial Connection Map
+
+- [bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Vercel AI Tutorial](../vercel-ai-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [Chapter 1: Getting Started and Deployment Paths](01-getting-started-and-deployment-paths.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: AI Pipeline and Phase Engine`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: AI Pipeline and Phase Engine
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `graph`, `User`, `Prompt` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: AI Pipeline and Phase Engine` as an operating subsystem inside **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Blueprint`, `Phase`, `Planning` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: AI Pipeline and Phase Engine` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `graph`.
+2. **Input normalization**: shape incoming data so `User` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Prompt`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [VibeSDK Repository](https://github.com/cloudflare/vibesdk)
+  Why it matters: authoritative reference on `VibeSDK Repository` (github.com).
+- [VibeSDK Releases](https://github.com/cloudflare/vibesdk/releases)
+  Why it matters: authoritative reference on `VibeSDK Releases` (github.com).
+- [VibeSDK Setup Guide](https://github.com/cloudflare/vibesdk/blob/main/docs/setup.md)
+  Why it matters: authoritative reference on `VibeSDK Setup Guide` (github.com).
+- [VibeSDK SDK Documentation](https://github.com/cloudflare/vibesdk/blob/main/sdk/README.md)
+  Why it matters: authoritative reference on `VibeSDK SDK Documentation` (github.com).
+- [Live Demo](https://build.cloudflare.dev/)
+  Why it matters: authoritative reference on `Live Demo` (build.cloudflare.dev).
+
+Suggested trace strategy:
+- search upstream code for `graph` and `User` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: System Architecture](02-system-architecture.md)
+- [Next Chapter: Chapter 4: Sandbox and Preview Runtime](04-sandbox-and-preview-runtime.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vibesdk-tutorial/04-sandbox-and-preview-runtime.md b/tutorials/vibesdk-tutorial/04-sandbox-and-preview-runtime.md
index c2858a5e..6a11bea7 100644
--- a/tutorials/vibesdk-tutorial/04-sandbox-and-preview-runtime.md
+++ b/tutorials/vibesdk-tutorial/04-sandbox-and-preview-runtime.md
@@ -7,6 +7,9 @@ parent: VibeSDK Tutorial
 
 # Chapter 4: Sandbox and Preview Runtime
 
+Welcome to **Chapter 4: Sandbox and Preview Runtime**. In this part of **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 VibeSDK runs generated projects in isolated preview runtimes so users can validate behavior before publishing.
 
 ## Learning Goals
@@ -82,3 +85,554 @@ Track these together, not in isolation:
 You now have a runtime model for sandbox previews and a practical baseline for stability tuning.
 
 Next: [Chapter 5: Data Layer and Persistence](05-data-layer-and-persistence.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- tutorial slug: **vibesdk-tutorial**
+- chapter focus: **Chapter 4: Sandbox and Preview Runtime**
+- system context: **Vibesdk Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Sandbox and Preview Runtime`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [VibeSDK Repository](https://github.com/cloudflare/vibesdk)
+- [VibeSDK Releases](https://github.com/cloudflare/vibesdk/releases)
+- [VibeSDK Setup Guide](https://github.com/cloudflare/vibesdk/blob/main/docs/setup.md)
+- [VibeSDK SDK Documentation](https://github.com/cloudflare/vibesdk/blob/main/sdk/README.md)
+- [Live Demo](https://build.cloudflare.dev/)
+
+### Cross-Tutorial Connection Map
+
+- [bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Vercel AI Tutorial](../vercel-ai-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [Chapter 1: Getting Started and Deployment Paths](01-getting-started-and-deployment-paths.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Sandbox and Preview Runtime`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Sandbox and Preview Runtime
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `graph`, `Generation`, `Agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Sandbox and Preview Runtime` as an operating subsystem inside **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Sandbox`, `Orchestrator`, `Container` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Sandbox and Preview Runtime` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `graph`.
+2. **Input normalization**: shape incoming data so `Generation` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [VibeSDK Repository](https://github.com/cloudflare/vibesdk)
+  Why it matters: authoritative reference on `VibeSDK Repository` (github.com).
+- [VibeSDK Releases](https://github.com/cloudflare/vibesdk/releases)
+  Why it matters: authoritative reference on `VibeSDK Releases` (github.com).
+- [VibeSDK Setup Guide](https://github.com/cloudflare/vibesdk/blob/main/docs/setup.md)
+  Why it matters: authoritative reference on `VibeSDK Setup Guide` (github.com).
+- [VibeSDK SDK Documentation](https://github.com/cloudflare/vibesdk/blob/main/sdk/README.md)
+  Why it matters: authoritative reference on `VibeSDK SDK Documentation` (github.com).
+- [Live Demo](https://build.cloudflare.dev/)
+  Why it matters: authoritative reference on `Live Demo` (build.cloudflare.dev).
+
+Suggested trace strategy:
+- search upstream code for `graph` and `Generation` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: AI Pipeline and Phase Engine](03-ai-pipeline-and-phase-engine.md)
+- [Next Chapter: Chapter 5: Data Layer and Persistence](05-data-layer-and-persistence.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vibesdk-tutorial/05-data-layer-and-persistence.md b/tutorials/vibesdk-tutorial/05-data-layer-and-persistence.md
index 94aee02f..1d5df11b 100644
--- a/tutorials/vibesdk-tutorial/05-data-layer-and-persistence.md
+++ b/tutorials/vibesdk-tutorial/05-data-layer-and-persistence.md
@@ -7,6 +7,9 @@ parent: VibeSDK Tutorial
 
 # Chapter 5: Data Layer and Persistence
 
+Welcome to **Chapter 5: Data Layer and Persistence**. In this part of **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 VibeSDK distributes persistence across D1, KV, R2, and Durable Object state to balance consistency, speed, and operational cost.
 
 ## Learning Goals
@@ -82,3 +85,554 @@ Treat remote migration as a controlled operation with rollback readiness.
 You now have a persistence model that supports reliable operations without overloading any single data layer.
 
 Next: [Chapter 6: API, SDK, and Integrations](06-api-sdk-and-integrations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- tutorial slug: **vibesdk-tutorial**
+- chapter focus: **Chapter 5: Data Layer and Persistence**
+- system context: **Vibesdk Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Data Layer and Persistence`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [VibeSDK Repository](https://github.com/cloudflare/vibesdk)
+- [VibeSDK Releases](https://github.com/cloudflare/vibesdk/releases)
+- [VibeSDK Setup Guide](https://github.com/cloudflare/vibesdk/blob/main/docs/setup.md)
+- [VibeSDK SDK Documentation](https://github.com/cloudflare/vibesdk/blob/main/sdk/README.md)
+- [Live Demo](https://build.cloudflare.dev/)
+
+### Cross-Tutorial Connection Map
+
+- [bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Vercel AI Tutorial](../vercel-ai-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [Chapter 1: Getting Started and Deployment Paths](01-getting-started-and-deployment-paths.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Data Layer and Persistence`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Data Layer and Persistence
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `migrate`, `graph`, `Frontend` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Data Layer and Persistence` as an operating subsystem inside **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Worker`, `Durable`, `Object` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Data Layer and Persistence` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `migrate`.
+2. **Input normalization**: shape incoming data so `graph` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Frontend`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [VibeSDK Repository](https://github.com/cloudflare/vibesdk)
+  Why it matters: authoritative reference on `VibeSDK Repository` (github.com).
+- [VibeSDK Releases](https://github.com/cloudflare/vibesdk/releases)
+  Why it matters: authoritative reference on `VibeSDK Releases` (github.com).
+- [VibeSDK Setup Guide](https://github.com/cloudflare/vibesdk/blob/main/docs/setup.md)
+  Why it matters: authoritative reference on `VibeSDK Setup Guide` (github.com).
+- [VibeSDK SDK Documentation](https://github.com/cloudflare/vibesdk/blob/main/sdk/README.md)
+  Why it matters: authoritative reference on `VibeSDK SDK Documentation` (github.com).
+- [Live Demo](https://build.cloudflare.dev/)
+  Why it matters: authoritative reference on `Live Demo` (build.cloudflare.dev).
+
+Suggested trace strategy:
+- search upstream code for `migrate` and `graph` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Sandbox and Preview Runtime](04-sandbox-and-preview-runtime.md)
+- [Next Chapter: Chapter 6: API, SDK, and Integrations](06-api-sdk-and-integrations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vibesdk-tutorial/06-api-sdk-and-integrations.md b/tutorials/vibesdk-tutorial/06-api-sdk-and-integrations.md
index bb112a0e..a7e857b6 100644
--- a/tutorials/vibesdk-tutorial/06-api-sdk-and-integrations.md
+++ b/tutorials/vibesdk-tutorial/06-api-sdk-and-integrations.md
@@ -7,6 +7,9 @@ parent: VibeSDK Tutorial
 
 # Chapter 6: API, SDK, and Integrations
 
+Welcome to **Chapter 6: API, SDK, and Integrations**. In this part of **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 VibeSDK can be embedded into workflows beyond the chat UI through APIs, the official TypeScript SDK, and automated handoff flows.
 
 ## Learning Goals
@@ -87,3 +90,554 @@ session.close();
 You now have a practical integration model for embedding VibeSDK into programmatic workflows and CI paths.
 
 Next: [Chapter 7: Security, Auth, and Governance](07-security-auth-and-governance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- tutorial slug: **vibesdk-tutorial**
+- chapter focus: **Chapter 6: API, SDK, and Integrations**
+- system context: **Vibesdk Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: API, SDK, and Integrations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [VibeSDK Repository](https://github.com/cloudflare/vibesdk)
+- [VibeSDK Releases](https://github.com/cloudflare/vibesdk/releases)
+- [VibeSDK Setup Guide](https://github.com/cloudflare/vibesdk/blob/main/docs/setup.md)
+- [VibeSDK SDK Documentation](https://github.com/cloudflare/vibesdk/blob/main/sdk/README.md)
+- [Live Demo](https://build.cloudflare.dev/)
+
+### Cross-Tutorial Connection Map
+
+- [bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Vercel AI Tutorial](../vercel-ai-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [Chapter 1: Getting Started and Deployment Paths](01-getting-started-and-deployment-paths.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: API, SDK, and Integrations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: API, SDK, and Integrations
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `session`, `vibesdk`, `PhasicClient` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: API, SDK, and Integrations` as an operating subsystem inside **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `client`, `build`, `wait` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: API, SDK, and Integrations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `session`.
+2. **Input normalization**: shape incoming data so `vibesdk` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `PhasicClient`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [VibeSDK Repository](https://github.com/cloudflare/vibesdk)
+  Why it matters: authoritative reference on `VibeSDK Repository` (github.com).
+- [VibeSDK Releases](https://github.com/cloudflare/vibesdk/releases)
+  Why it matters: authoritative reference on `VibeSDK Releases` (github.com).
+- [VibeSDK Setup Guide](https://github.com/cloudflare/vibesdk/blob/main/docs/setup.md)
+  Why it matters: authoritative reference on `VibeSDK Setup Guide` (github.com).
+- [VibeSDK SDK Documentation](https://github.com/cloudflare/vibesdk/blob/main/sdk/README.md)
+  Why it matters: authoritative reference on `VibeSDK SDK Documentation` (github.com).
+- [Live Demo](https://build.cloudflare.dev/)
+  Why it matters: authoritative reference on `Live Demo` (build.cloudflare.dev).
+
+Suggested trace strategy:
+- search upstream code for `session` and `vibesdk` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Data Layer and Persistence](05-data-layer-and-persistence.md)
+- [Next Chapter: Chapter 7: Security, Auth, and Governance](07-security-auth-and-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vibesdk-tutorial/07-security-auth-and-governance.md b/tutorials/vibesdk-tutorial/07-security-auth-and-governance.md
index 79461af6..039cd460 100644
--- a/tutorials/vibesdk-tutorial/07-security-auth-and-governance.md
+++ b/tutorials/vibesdk-tutorial/07-security-auth-and-governance.md
@@ -7,6 +7,9 @@ parent: VibeSDK Tutorial
 
 # Chapter 7: Security, Auth, and Governance
 
+Welcome to **Chapter 7: Security, Auth, and Governance**. In this part of **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 VibeSDK security is a cross-layer concern: identity, secret management, execution controls, and policy enforcement all matter.
 
 ## Learning Goals
@@ -70,3 +73,562 @@ At minimum, enforce:
 You now have a practical security and governance baseline for operating VibeSDK beyond a single-user demo setup.
 
 Next: [Chapter 8: Production Operations and Scaling](08-production-operations-and-scaling.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- tutorial slug: **vibesdk-tutorial**
+- chapter focus: **Chapter 7: Security, Auth, and Governance**
+- system context: **Vibesdk Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Security, Auth, and Governance`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [VibeSDK Repository](https://github.com/cloudflare/vibesdk)
+- [VibeSDK Releases](https://github.com/cloudflare/vibesdk/releases)
+- [VibeSDK Setup Guide](https://github.com/cloudflare/vibesdk/blob/main/docs/setup.md)
+- [VibeSDK SDK Documentation](https://github.com/cloudflare/vibesdk/blob/main/sdk/README.md)
+- [Live Demo](https://build.cloudflare.dev/)
+
+### Cross-Tutorial Connection Map
+
+- [bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Vercel AI Tutorial](../vercel-ai-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [Chapter 1: Getting Started and Deployment Paths](01-getting-started-and-deployment-paths.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Security, Auth, and Governance`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Security, Auth, and Governance
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Security, Auth, and Governance` as an operating subsystem inside **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Security, Auth, and Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [VibeSDK Repository](https://github.com/cloudflare/vibesdk)
+  Why it matters: authoritative reference on `VibeSDK Repository` (github.com).
+- [VibeSDK Releases](https://github.com/cloudflare/vibesdk/releases)
+  Why it matters: authoritative reference on `VibeSDK Releases` (github.com).
+- [VibeSDK Setup Guide](https://github.com/cloudflare/vibesdk/blob/main/docs/setup.md)
+  Why it matters: authoritative reference on `VibeSDK Setup Guide` (github.com).
+- [VibeSDK SDK Documentation](https://github.com/cloudflare/vibesdk/blob/main/sdk/README.md)
+  Why it matters: authoritative reference on `VibeSDK SDK Documentation` (github.com).
+- [Live Demo](https://build.cloudflare.dev/)
+  Why it matters: authoritative reference on `Live Demo` (build.cloudflare.dev).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: API, SDK, and Integrations](06-api-sdk-and-integrations.md)
+- [Next Chapter: Chapter 8: Production Operations and Scaling](08-production-operations-and-scaling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vibesdk-tutorial/08-production-operations-and-scaling.md b/tutorials/vibesdk-tutorial/08-production-operations-and-scaling.md
index e4f7f0ee..d7af458a 100644
--- a/tutorials/vibesdk-tutorial/08-production-operations-and-scaling.md
+++ b/tutorials/vibesdk-tutorial/08-production-operations-and-scaling.md
@@ -7,6 +7,9 @@ parent: VibeSDK Tutorial
 
 # Chapter 8: Production Operations and Scaling
 
+Welcome to **Chapter 8: Production Operations and Scaling**. In this part of **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter turns VibeSDK from a working deployment into a managed, production-ready platform.
 
 ## Learning Goals
@@ -84,3 +87,553 @@ Run these in CI and again for release candidates with production-like bindings.
 You now have an operations blueprint for running VibeSDK as a production platform with measurable reliability and governance.
 
 Next: return to the [VibeSDK Tutorial index](index.md).
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- tutorial slug: **vibesdk-tutorial**
+- chapter focus: **Chapter 8: Production Operations and Scaling**
+- system context: **Vibesdk Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Operations and Scaling`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [VibeSDK Repository](https://github.com/cloudflare/vibesdk)
+- [VibeSDK Releases](https://github.com/cloudflare/vibesdk/releases)
+- [VibeSDK Setup Guide](https://github.com/cloudflare/vibesdk/blob/main/docs/setup.md)
+- [VibeSDK SDK Documentation](https://github.com/cloudflare/vibesdk/blob/main/sdk/README.md)
+- [Live Demo](https://build.cloudflare.dev/)
+
+### Cross-Tutorial Connection Map
+
+- [bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [Dyad Tutorial](../dyad-tutorial/)
+- [Vercel AI Tutorial](../vercel-ai-tutorial/)
+- [OpenHands Tutorial](../openhands-tutorial/)
+- [Chapter 1: Getting Started and Deployment Paths](01-getting-started-and-deployment-paths.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Operations and Scaling`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Production Operations and Scaling
+
+- tutorial context: **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `lint`, `typecheck`, `test` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Operations and Scaling` as an operating subsystem inside **VibeSDK Tutorial: Build a Vibe-Coding Platform on Cloudflare**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `migrate`, `remote` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Operations and Scaling` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `lint`.
+2. **Input normalization**: shape incoming data so `typecheck` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `test`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [VibeSDK Repository](https://github.com/cloudflare/vibesdk)
+  Why it matters: authoritative reference on `VibeSDK Repository` (github.com).
+- [VibeSDK Releases](https://github.com/cloudflare/vibesdk/releases)
+  Why it matters: authoritative reference on `VibeSDK Releases` (github.com).
+- [VibeSDK Setup Guide](https://github.com/cloudflare/vibesdk/blob/main/docs/setup.md)
+  Why it matters: authoritative reference on `VibeSDK Setup Guide` (github.com).
+- [VibeSDK SDK Documentation](https://github.com/cloudflare/vibesdk/blob/main/sdk/README.md)
+  Why it matters: authoritative reference on `VibeSDK SDK Documentation` (github.com).
+- [Live Demo](https://build.cloudflare.dev/)
+  Why it matters: authoritative reference on `Live Demo` (build.cloudflare.dev).
+
+Suggested trace strategy:
+- search upstream code for `lint` and `typecheck` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Security, Auth, and Governance](07-security-auth-and-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vllm-tutorial/01-getting-started.md b/tutorials/vllm-tutorial/01-getting-started.md
index cefcdfcc..82cd15b9 100644
--- a/tutorials/vllm-tutorial/01-getting-started.md
+++ b/tutorials/vllm-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: vLLM Tutorial
 
 # Chapter 1: Getting Started with vLLM
 
+Welcome to **Chapter 1: Getting Started with vLLM**. In this part of **vLLM Tutorial: High-Performance LLM Inference**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Install vLLM, understand its architecture, and run your first high-performance LLM inference.
 
 ## Overview
@@ -550,4 +553,51 @@ Next, we'll explore **model loading** options - working with different model for
 
 **Ready for the next chapter?** [Chapter 2: Model Loading](02-model-loading.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `print`, `SamplingParams`, `output` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with vLLM` as an operating subsystem inside **vLLM Tutorial: High-Performance LLM Inference**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `temperature`, `outputs`, `model` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with vLLM` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `print`.
+2. **Input normalization**: shape incoming data so `SamplingParams` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `output`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/vllm-project/vllm)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `print` and `SamplingParams` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Model Loading and Management](02-model-loading.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vllm-tutorial/02-model-loading.md b/tutorials/vllm-tutorial/02-model-loading.md
index ea1e13c8..08b9dcaa 100644
--- a/tutorials/vllm-tutorial/02-model-loading.md
+++ b/tutorials/vllm-tutorial/02-model-loading.md
@@ -8,6 +8,9 @@ parent: vLLM Tutorial
 
 # Chapter 2: Model Loading and Management
 
+Welcome to **Chapter 2: Model Loading and Management**. In this part of **vLLM Tutorial: High-Performance LLM Inference**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master loading different model formats, quantization techniques, and efficient model management in vLLM.
 
 ## Overview
@@ -754,4 +757,52 @@ Next, we'll explore **basic inference** - text generation, sampling strategies,
 
 **Ready for the next chapter?** [Chapter 3: Basic Inference](03-basic-inference.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `model`, `print`, `model_name` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Model Loading and Management` as an operating subsystem inside **vLLM Tutorial: High-Performance LLM Inference**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `self`, `model_dir`, `config` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Model Loading and Management` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `model`.
+2. **Input normalization**: shape incoming data so `print` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `model_name`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/vllm-project/vllm)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `model` and `print` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with vLLM](01-getting-started.md)
+- [Next Chapter: Chapter 3: Basic Inference - Text Generation and Sampling](03-basic-inference.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vllm-tutorial/03-basic-inference.md b/tutorials/vllm-tutorial/03-basic-inference.md
index 7d0960e4..9e7c6e27 100644
--- a/tutorials/vllm-tutorial/03-basic-inference.md
+++ b/tutorials/vllm-tutorial/03-basic-inference.md
@@ -8,6 +8,9 @@ parent: vLLM Tutorial
 
 # Chapter 3: Basic Inference - Text Generation and Sampling
 
+Welcome to **Chapter 3: Basic Inference - Text Generation and Sampling**. In this part of **vLLM Tutorial: High-Performance LLM Inference**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master text generation with vLLM, including sampling strategies, parameter tuning, and controlling generation behavior.
 
 ## Overview
@@ -619,4 +622,52 @@ Next, we'll explore **advanced features** - streaming, tool calling, and multi-m
 
 **Ready for the next chapter?** [Chapter 4: Advanced Features](04-advanced-features.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `print`, `SamplingParams`, `result` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Basic Inference - Text Generation and Sampling` as an operating subsystem inside **vLLM Tutorial: High-Performance LLM Inference**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `response`, `max_tokens`, `outputs` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Basic Inference - Text Generation and Sampling` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `print`.
+2. **Input normalization**: shape incoming data so `SamplingParams` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `result`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/vllm-project/vllm)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `print` and `SamplingParams` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Model Loading and Management](02-model-loading.md)
+- [Next Chapter: Chapter 4: Advanced Features - Streaming, Tool Calling, and Multi-Modal](04-advanced-features.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vllm-tutorial/04-advanced-features.md b/tutorials/vllm-tutorial/04-advanced-features.md
index 06e0593f..54368e78 100644
--- a/tutorials/vllm-tutorial/04-advanced-features.md
+++ b/tutorials/vllm-tutorial/04-advanced-features.md
@@ -8,6 +8,9 @@ parent: vLLM Tutorial
 
 # Chapter 4: Advanced Features - Streaming, Tool Calling, and Multi-Modal
 
+Welcome to **Chapter 4: Advanced Features - Streaming, Tool Calling, and Multi-Modal**. In this part of **vLLM Tutorial: High-Performance LLM Inference**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Explore vLLM's advanced capabilities including real-time streaming, function calling, and multi-modal models.
 
 ## Overview
@@ -783,4 +786,52 @@ Next, we'll explore **performance optimization** - batching, quantization, and G
 
 **Ready for the next chapter?** [Chapter 5: Performance Optimization](05-performance-optimization.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `print`, `result` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Advanced Features - Streaming, Tool Calling, and Multi-Modal` as an operating subsystem inside **vLLM Tutorial: High-Performance LLM Inference**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `tool`, `text`, `prompt` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Advanced Features - Streaming, Tool Calling, and Multi-Modal` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `print` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `result`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/vllm-project/vllm)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `print` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Basic Inference - Text Generation and Sampling](03-basic-inference.md)
+- [Next Chapter: Chapter 5: Performance Optimization - Maximizing Throughput and Efficiency](05-performance-optimization.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vllm-tutorial/05-performance-optimization.md b/tutorials/vllm-tutorial/05-performance-optimization.md
index b5e92838..448758d8 100644
--- a/tutorials/vllm-tutorial/05-performance-optimization.md
+++ b/tutorials/vllm-tutorial/05-performance-optimization.md
@@ -8,6 +8,9 @@ parent: vLLM Tutorial
 
 # Chapter 5: Performance Optimization - Maximizing Throughput and Efficiency
 
+Welcome to **Chapter 5: Performance Optimization - Maximizing Throughput and Efficiency**. In this part of **vLLM Tutorial: High-Performance LLM Inference**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master advanced optimization techniques for vLLM including batching strategies, quantization, GPU optimization, and memory management.
 
 ## Overview
@@ -888,4 +891,52 @@ Next, we'll explore **distributed inference** - scaling vLLM across multiple GPU
 
 **Ready for the next chapter?** [Chapter 6: Distributed Inference](06-distributed-inference.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `print`, `result` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Performance Optimization - Maximizing Throughput and Efficiency` as an operating subsystem inside **vLLM Tutorial: High-Performance LLM Inference**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `time`, `results`, `sampling_params` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Performance Optimization - Maximizing Throughput and Efficiency` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `print` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `result`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/vllm-project/vllm)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `print` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Advanced Features - Streaming, Tool Calling, and Multi-Modal](04-advanced-features.md)
+- [Next Chapter: Chapter 6: Distributed Inference - Scaling Across GPUs and Nodes](06-distributed-inference.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vllm-tutorial/06-distributed-inference.md b/tutorials/vllm-tutorial/06-distributed-inference.md
index a894cf5b..4c536d4a 100644
--- a/tutorials/vllm-tutorial/06-distributed-inference.md
+++ b/tutorials/vllm-tutorial/06-distributed-inference.md
@@ -8,6 +8,9 @@ parent: vLLM Tutorial
 
 # Chapter 6: Distributed Inference - Scaling Across GPUs and Nodes
 
+Welcome to **Chapter 6: Distributed Inference - Scaling Across GPUs and Nodes**. In this part of **vLLM Tutorial: High-Performance LLM Inference**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master distributed inference techniques with vLLM including multi-GPU setups, tensor parallelism, and cluster deployments.
 
 ## Overview
@@ -947,4 +950,52 @@ Next, we'll explore **production deployment** - serving vLLM with FastAPI, Docke
 
 **Ready for the next chapter?** [Chapter 7: Production Deployment](07-production-deployment.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `print`, `self`, `instance_id` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Distributed Inference - Scaling Across GPUs and Nodes` as an operating subsystem inside **vLLM Tutorial: High-Performance LLM Inference**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `vllm`, `instance`, `result` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Distributed Inference - Scaling Across GPUs and Nodes` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `print`.
+2. **Input normalization**: shape incoming data so `self` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `instance_id`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/vllm-project/vllm)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `print` and `self` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Performance Optimization - Maximizing Throughput and Efficiency](05-performance-optimization.md)
+- [Next Chapter: Chapter 7: Production Deployment - Serving vLLM at Scale](07-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vllm-tutorial/07-production-deployment.md b/tutorials/vllm-tutorial/07-production-deployment.md
index bdcda2d7..debcce3f 100644
--- a/tutorials/vllm-tutorial/07-production-deployment.md
+++ b/tutorials/vllm-tutorial/07-production-deployment.md
@@ -8,6 +8,9 @@ parent: vLLM Tutorial
 
 # Chapter 7: Production Deployment - Serving vLLM at Scale
 
+Welcome to **Chapter 7: Production Deployment - Serving vLLM at Scale**. In this part of **vLLM Tutorial: High-Performance LLM Inference**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy vLLM in production with FastAPI, Docker, Kubernetes, and enterprise-grade operational practices.
 
 ## Overview
@@ -1276,4 +1279,52 @@ Next, we'll explore **monitoring and scaling** - performance monitoring and auto
 
 **Ready for the next chapter?** [Chapter 8: Monitoring & Scaling](08-monitoring-scaling.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `request`, `vllm` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Production Deployment - Serving vLLM at Scale` as an operating subsystem inside **vLLM Tutorial: High-Performance LLM Inference**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `time`, `logger`, `name` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Production Deployment - Serving vLLM at Scale` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `request` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `vllm`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/vllm-project/vllm)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `request` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Distributed Inference - Scaling Across GPUs and Nodes](06-distributed-inference.md)
+- [Next Chapter: Chapter 8: Monitoring & Scaling - Production Operations at Scale](08-monitoring-scaling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/vllm-tutorial/08-monitoring-scaling.md b/tutorials/vllm-tutorial/08-monitoring-scaling.md
index 10833d78..c70a8484 100644
--- a/tutorials/vllm-tutorial/08-monitoring-scaling.md
+++ b/tutorials/vllm-tutorial/08-monitoring-scaling.md
@@ -8,6 +8,9 @@ parent: vLLM Tutorial
 
 # Chapter 8: Monitoring & Scaling - Production Operations at Scale
 
+Welcome to **Chapter 8: Monitoring & Scaling - Production Operations at Scale**. In this part of **vLLM Tutorial: High-Performance LLM Inference**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master comprehensive monitoring, performance optimization, and auto-scaling for vLLM deployments in production environments.
 
 ## Overview
@@ -1072,4 +1075,51 @@ This concludes our comprehensive vLLM tutorial series. You've learned everything
 
 ---
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `metrics`, `time` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Monitoring & Scaling - Production Operations at Scale` as an operating subsystem inside **vLLM Tutorial: High-Performance LLM Inference**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `report`, `decision`, `model` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Monitoring & Scaling - Production Operations at Scale` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `metrics` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `time`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/vllm-project/vllm)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `metrics` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Production Deployment - Serving vLLM at Scale](07-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/whisper-cpp-tutorial/01-getting-started.md b/tutorials/whisper-cpp-tutorial/01-getting-started.md
index cb4a4ad5..8fb5c63b 100644
--- a/tutorials/whisper-cpp-tutorial/01-getting-started.md
+++ b/tutorials/whisper-cpp-tutorial/01-getting-started.md
@@ -322,3 +322,48 @@ Now that you have Whisper.cpp working, let's dive deeper into audio processing c
 4. Modify the simple transcriber to add custom formatting
 
 *What kind of speech recognition application are you most excited to build?* 🎤
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `model`, `audio`, `main` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Whisper.cpp` as an operating subsystem inside **Whisper.cpp Tutorial: High-Performance Speech Recognition in C/C++**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `base`, `models`, `ggml` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Whisper.cpp` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `model`.
+2. **Input normalization**: shape incoming data so `audio` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `main`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ggml-org/whisper.cpp)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `model` and `audio` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Audio Processing Fundamentals](02-audio-processing.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/whisper-cpp-tutorial/02-audio-processing.md b/tutorials/whisper-cpp-tutorial/02-audio-processing.md
index 2dc17a56..364bcaaa 100644
--- a/tutorials/whisper-cpp-tutorial/02-audio-processing.md
+++ b/tutorials/whisper-cpp-tutorial/02-audio-processing.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Audio Processing Fundamentals
 
+Welcome to **Chapter 2: Audio Processing Fundamentals**. In this part of **Whisper.cpp Tutorial: High-Performance Speech Recognition in C/C++**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Welcome back! Now that you have Whisper.cpp up and running, let's dive into the fascinating world of audio processing. Understanding how audio works is crucial for getting the best results from speech recognition systems. In this chapter, we'll explore the fundamentals of digital audio and how Whisper.cpp processes sound.
 
 ## What Makes Audio Processing Important?
@@ -511,3 +514,49 @@ Now that you understand how audio processing works, let's explore the neural net
 4. Experiment with audio preprocessing techniques on noisy recordings
 
 *How does understanding audio processing change how you think about speech recognition?* 🔊
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `audio`, `librosa`, `self` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Audio Processing Fundamentals` as an operating subsystem inside **Whisper.cpp Tutorial: High-Performance Speech Recognition in C/C++**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `print`, `issues`, `recording` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Audio Processing Fundamentals` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `audio`.
+2. **Input normalization**: shape incoming data so `librosa` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `self`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ggml-org/whisper.cpp)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `audio` and `librosa` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Whisper.cpp](01-getting-started.md)
+- [Next Chapter: Chapter 3: Model Architecture & GGML](03-model-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/whisper-cpp-tutorial/03-model-architecture.md b/tutorials/whisper-cpp-tutorial/03-model-architecture.md
index c66b1fc3..6879f2f1 100644
--- a/tutorials/whisper-cpp-tutorial/03-model-architecture.md
+++ b/tutorials/whisper-cpp-tutorial/03-model-architecture.md
@@ -8,6 +8,9 @@ parent: "Whisper.cpp Tutorial"
 
 # Chapter 3: Model Architecture & GGML
 
+Welcome to **Chapter 3: Model Architecture & GGML**. In this part of **Whisper.cpp Tutorial: High-Performance Speech Recognition in C/C++**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Understanding how Whisper works internally and the GGML tensor library that powers Whisper.cpp
 
 ## 🎯 Learning Objectives
@@ -571,4 +574,50 @@ void whisper_benchmark_print(struct whisper_benchmark * b) {
 
 ---
 
-**Ready to use the core API?** Continue to [Chapter 4: Core API & Usage Patterns](04-core-api.md)
\ No newline at end of file
+**Ready to use the core API?** Continue to [Chapter 4: Core API & Usage Patterns](04-core-api.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `ggml_tensor`, `embed_dim` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Model Architecture & GGML` as an operating subsystem inside **Whisper.cpp Tutorial: High-Performance Speech Recognition in C/C++**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `float`, `struct`, `uint8_t` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Model Architecture & GGML` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `ggml_tensor` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `embed_dim`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ggml-org/whisper.cpp)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `ggml_tensor` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Audio Processing Fundamentals](02-audio-processing.md)
+- [Next Chapter: Chapter 4: Core API & Usage Patterns](04-core-api.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/whisper-cpp-tutorial/04-core-api.md b/tutorials/whisper-cpp-tutorial/04-core-api.md
index c090f086..b5aff211 100644
--- a/tutorials/whisper-cpp-tutorial/04-core-api.md
+++ b/tutorials/whisper-cpp-tutorial/04-core-api.md
@@ -8,6 +8,9 @@ parent: "Whisper.cpp Tutorial"
 
 # Chapter 4: Core API & Usage Patterns
 
+Welcome to **Chapter 4: Core API & Usage Patterns**. In this part of **Whisper.cpp Tutorial: High-Performance Speech Recognition in C/C++**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Mastering Whisper.cpp's C/C++ API for speech recognition applications
 
 ## 🎯 Learning Objectives
@@ -764,4 +767,50 @@ int main(int argc, char * argv[]) {
 
 ---
 
-**Ready for real-time streaming?** Continue to [Chapter 5: Real-Time Streaming](05-real-time-streaming.md)
\ No newline at end of file
+**Ready for real-time streaming?** Continue to [Chapter 5: Real-Time Streaming](05-real-time-streaming.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `result`, `struct`, `audio` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Core API & Usage Patterns` as an operating subsystem inside **Whisper.cpp Tutorial: High-Performance Speech Recognition in C/C++**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `wparams`, `pcmf32`, `char` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Core API & Usage Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `result`.
+2. **Input normalization**: shape incoming data so `struct` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `audio`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ggml-org/whisper.cpp)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `result` and `struct` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Model Architecture & GGML](03-model-architecture.md)
+- [Next Chapter: Chapter 5: Real-Time Streaming](05-real-time-streaming.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/whisper-cpp-tutorial/05-real-time-streaming.md b/tutorials/whisper-cpp-tutorial/05-real-time-streaming.md
index f03b1d46..8dfbdb91 100644
--- a/tutorials/whisper-cpp-tutorial/05-real-time-streaming.md
+++ b/tutorials/whisper-cpp-tutorial/05-real-time-streaming.md
@@ -8,6 +8,9 @@ parent: "Whisper.cpp Tutorial"
 
 # Chapter 5: Real-Time Streaming
 
+Welcome to **Chapter 5: Real-Time Streaming**. In this part of **Whisper.cpp Tutorial: High-Performance Speech Recognition in C/C++**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Stream processing, voice activity detection, and real-time transcription with Whisper.cpp
 
 ## Learning Objectives
@@ -846,3 +849,49 @@ Now that you can transcribe audio in real time, let's explore how Whisper.cpp ha
 ---
 
 *Built with insights from the [whisper.cpp](https://github.com/ggerganov/whisper.cpp) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `float`, `self`, `config` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Real-Time Streaming` as an operating subsystem inside **Whisper.cpp Tutorial: High-Performance Speech Recognition in C/C++**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `audio`, `wparams`, `result` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Real-Time Streaming` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `float`.
+2. **Input normalization**: shape incoming data so `self` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `config`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ggml-org/whisper.cpp)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `float` and `self` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Core API & Usage Patterns](04-core-api.md)
+- [Next Chapter: Chapter 6: Language & Translation](06-language-translation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/whisper-cpp-tutorial/06-language-translation.md b/tutorials/whisper-cpp-tutorial/06-language-translation.md
index 7d40e676..5409718d 100644
--- a/tutorials/whisper-cpp-tutorial/06-language-translation.md
+++ b/tutorials/whisper-cpp-tutorial/06-language-translation.md
@@ -8,6 +8,9 @@ parent: "Whisper.cpp Tutorial"
 
 # Chapter 6: Language & Translation
 
+Welcome to **Chapter 6: Language & Translation**. In this part of **Whisper.cpp Tutorial: High-Performance Speech Recognition in C/C++**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Multi-language support, translation mode, language detection, and speaker diarization with Whisper.cpp
 
 ## Learning Objectives
@@ -785,3 +788,49 @@ With multilingual transcription and translation covered, let's explore how to de
 ---
 
 *Built with insights from the [whisper.cpp](https://github.com/ggerganov/whisper.cpp) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `audio`, `results`, `self` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Language & Translation` as an operating subsystem inside **Whisper.cpp Tutorial: High-Performance Speech Recognition in C/C++**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `language`, `wparams`, `result` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Language & Translation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `audio`.
+2. **Input normalization**: shape incoming data so `results` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `self`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ggml-org/whisper.cpp)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `audio` and `results` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Real-Time Streaming](05-real-time-streaming.md)
+- [Next Chapter: Chapter 7: Platform Integration](07-platform-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/whisper-cpp-tutorial/07-platform-integration.md b/tutorials/whisper-cpp-tutorial/07-platform-integration.md
index ef13a144..a845e628 100644
--- a/tutorials/whisper-cpp-tutorial/07-platform-integration.md
+++ b/tutorials/whisper-cpp-tutorial/07-platform-integration.md
@@ -8,6 +8,9 @@ parent: "Whisper.cpp Tutorial"
 
 # Chapter 7: Platform Integration
 
+Welcome to **Chapter 7: Platform Integration**. In this part of **Whisper.cpp Tutorial: High-Performance Speech Recognition in C/C++**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > iOS, Android, WebAssembly, Python, and Node.js bindings for Whisper.cpp
 
 ## Learning Objectives
@@ -874,3 +877,49 @@ With platform integration covered, it is time to prepare Whisper.cpp for product
 ---
 
 *Built with insights from the [whisper.cpp](https://github.com/ggerganov/whisper.cpp) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `whisper`, `audio` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Platform Integration` as an operating subsystem inside **Whisper.cpp Tutorial: High-Performance Speech Recognition in C/C++**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `samples`, `build`, `ctypes` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Platform Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `whisper` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `audio`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ggml-org/whisper.cpp)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `whisper` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Language & Translation](06-language-translation.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-deployment-production.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/whisper-cpp-tutorial/08-deployment-production.md b/tutorials/whisper-cpp-tutorial/08-deployment-production.md
index d0001806..0c6641dd 100644
--- a/tutorials/whisper-cpp-tutorial/08-deployment-production.md
+++ b/tutorials/whisper-cpp-tutorial/08-deployment-production.md
@@ -8,6 +8,9 @@ parent: "Whisper.cpp Tutorial"
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **Whisper.cpp Tutorial: High-Performance Speech Recognition in C/C++**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Server mode, batch processing, GPU acceleration, scaling patterns, and benchmarking for Whisper.cpp
 
 ## Learning Objectives
@@ -1013,3 +1016,48 @@ Deploying Whisper.cpp in production involves choosing the right model size and q
 ---
 
 *Built with insights from the [whisper.cpp](https://github.com/ggerganov/whisper.cpp) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `language`, `audio`, `json` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **Whisper.cpp Tutorial: High-Performance Speech Recognition in C/C++**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `result`, `server`, `models` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `language`.
+2. **Input normalization**: shape incoming data so `audio` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `json`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ggml-org/whisper.cpp)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `language` and `audio` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Platform Integration](07-platform-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/wshobson-agents-tutorial/01-getting-started.md b/tutorials/wshobson-agents-tutorial/01-getting-started.md
index 1918d1d6..4968cde6 100644
--- a/tutorials/wshobson-agents-tutorial/01-getting-started.md
+++ b/tutorials/wshobson-agents-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Wshobson Agents Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets the marketplace connected and installs your first focused plugin set.
 
 ## Learning Goals
@@ -52,3 +55,592 @@ This set is enough for many day-one coding loops.
 You now have a working baseline installation and first command surface.
 
 Next: [Chapter 2: Marketplace Architecture and Plugin Structure](02-marketplace-architecture-and-plugin-structure.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- tutorial slug: **wshobson-agents-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Wshobson Agents Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Repository README](https://github.com/wshobson/agents/blob/main/README.md)
+- [Plugin Reference](https://github.com/wshobson/agents/blob/main/docs/plugins.md)
+- [Usage Guide](https://github.com/wshobson/agents/blob/main/docs/usage.md)
+- [Agent Reference](https://github.com/wshobson/agents/blob/main/docs/agents.md)
+- [Agent Skills](https://github.com/wshobson/agents/blob/main/docs/agent-skills.md)
+- [Architecture Guide](https://github.com/wshobson/agents/blob/main/docs/architecture.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [AGENTS.md Tutorial](../agents-md-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `plugin`, `install`, `marketplace` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `wshobson`, `agents`, `python` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `plugin`.
+2. **Input normalization**: shape incoming data so `install` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `marketplace`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Repository README](https://github.com/wshobson/agents/blob/main/README.md)
+  Why it matters: authoritative reference on `Repository README` (github.com).
+- [Plugin Reference](https://github.com/wshobson/agents/blob/main/docs/plugins.md)
+  Why it matters: authoritative reference on `Plugin Reference` (github.com).
+- [Usage Guide](https://github.com/wshobson/agents/blob/main/docs/usage.md)
+  Why it matters: authoritative reference on `Usage Guide` (github.com).
+- [Agent Reference](https://github.com/wshobson/agents/blob/main/docs/agents.md)
+  Why it matters: authoritative reference on `Agent Reference` (github.com).
+- [Agent Skills](https://github.com/wshobson/agents/blob/main/docs/agent-skills.md)
+  Why it matters: authoritative reference on `Agent Skills` (github.com).
+- [Architecture Guide](https://github.com/wshobson/agents/blob/main/docs/architecture.md)
+  Why it matters: authoritative reference on `Architecture Guide` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `plugin` and `install` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Marketplace Architecture and Plugin Structure](02-marketplace-architecture-and-plugin-structure.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/wshobson-agents-tutorial/02-marketplace-architecture-and-plugin-structure.md b/tutorials/wshobson-agents-tutorial/02-marketplace-architecture-and-plugin-structure.md
index 3264aa68..dd616aa0 100644
--- a/tutorials/wshobson-agents-tutorial/02-marketplace-architecture-and-plugin-structure.md
+++ b/tutorials/wshobson-agents-tutorial/02-marketplace-architecture-and-plugin-structure.md
@@ -7,6 +7,9 @@ parent: Wshobson Agents Tutorial
 
 # Chapter 2: Marketplace Architecture and Plugin Structure
 
+Welcome to **Chapter 2: Marketplace Architecture and Plugin Structure**. In this part of **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains the repository's composable plugin architecture.
 
 ## Learning Goals
@@ -53,3 +56,589 @@ The project emphasizes:
 You now understand the composable architecture that powers the ecosystem.
 
 Next: [Chapter 3: Installation and Plugin Selection Strategy](03-installation-and-plugin-selection-strategy.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- tutorial slug: **wshobson-agents-tutorial**
+- chapter focus: **Chapter 2: Marketplace Architecture and Plugin Structure**
+- system context: **Wshobson Agents Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Marketplace Architecture and Plugin Structure`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Repository README](https://github.com/wshobson/agents/blob/main/README.md)
+- [Plugin Reference](https://github.com/wshobson/agents/blob/main/docs/plugins.md)
+- [Usage Guide](https://github.com/wshobson/agents/blob/main/docs/usage.md)
+- [Agent Reference](https://github.com/wshobson/agents/blob/main/docs/agents.md)
+- [Agent Skills](https://github.com/wshobson/agents/blob/main/docs/agent-skills.md)
+- [Architecture Guide](https://github.com/wshobson/agents/blob/main/docs/architecture.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [AGENTS.md Tutorial](../agents-md-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Marketplace Architecture and Plugin Structure`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Marketplace Architecture and Plugin Structure
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Marketplace Architecture and Plugin Structure` as an operating subsystem inside **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Marketplace Architecture and Plugin Structure` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Repository README](https://github.com/wshobson/agents/blob/main/README.md)
+  Why it matters: authoritative reference on `Repository README` (github.com).
+- [Plugin Reference](https://github.com/wshobson/agents/blob/main/docs/plugins.md)
+  Why it matters: authoritative reference on `Plugin Reference` (github.com).
+- [Usage Guide](https://github.com/wshobson/agents/blob/main/docs/usage.md)
+  Why it matters: authoritative reference on `Usage Guide` (github.com).
+- [Agent Reference](https://github.com/wshobson/agents/blob/main/docs/agents.md)
+  Why it matters: authoritative reference on `Agent Reference` (github.com).
+- [Agent Skills](https://github.com/wshobson/agents/blob/main/docs/agent-skills.md)
+  Why it matters: authoritative reference on `Agent Skills` (github.com).
+- [Architecture Guide](https://github.com/wshobson/agents/blob/main/docs/architecture.md)
+  Why it matters: authoritative reference on `Architecture Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Installation and Plugin Selection Strategy](03-installation-and-plugin-selection-strategy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/wshobson-agents-tutorial/03-installation-and-plugin-selection-strategy.md b/tutorials/wshobson-agents-tutorial/03-installation-and-plugin-selection-strategy.md
index e5a17fa7..f47f3d22 100644
--- a/tutorials/wshobson-agents-tutorial/03-installation-and-plugin-selection-strategy.md
+++ b/tutorials/wshobson-agents-tutorial/03-installation-and-plugin-selection-strategy.md
@@ -7,6 +7,9 @@ parent: Wshobson Agents Tutorial
 
 # Chapter 3: Installation and Plugin Selection Strategy
 
+Welcome to **Chapter 3: Installation and Plugin Selection Strategy**. In this part of **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter shows how to choose plugin portfolios by objective instead of installing everything.
 
 ## Learning Goals
@@ -62,3 +65,577 @@ This chapter shows how to choose plugin portfolios by objective instead of insta
 You now have a practical method for controlled plugin adoption.
 
 Next: [Chapter 4: Commands, Natural Language, and Workflow Orchestration](04-commands-natural-language-and-workflow-orchestration.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- tutorial slug: **wshobson-agents-tutorial**
+- chapter focus: **Chapter 3: Installation and Plugin Selection Strategy**
+- system context: **Wshobson Agents Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Installation and Plugin Selection Strategy`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Repository README](https://github.com/wshobson/agents/blob/main/README.md)
+- [Plugin Reference](https://github.com/wshobson/agents/blob/main/docs/plugins.md)
+- [Usage Guide](https://github.com/wshobson/agents/blob/main/docs/usage.md)
+- [Agent Reference](https://github.com/wshobson/agents/blob/main/docs/agents.md)
+- [Agent Skills](https://github.com/wshobson/agents/blob/main/docs/agent-skills.md)
+- [Architecture Guide](https://github.com/wshobson/agents/blob/main/docs/architecture.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [AGENTS.md Tutorial](../agents-md-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Installation and Plugin Selection Strategy`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Installation and Plugin Selection Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Installation and Plugin Selection Strategy` as an operating subsystem inside **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Installation and Plugin Selection Strategy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Repository README](https://github.com/wshobson/agents/blob/main/README.md)
+  Why it matters: authoritative reference on `Repository README` (github.com).
+- [Plugin Reference](https://github.com/wshobson/agents/blob/main/docs/plugins.md)
+  Why it matters: authoritative reference on `Plugin Reference` (github.com).
+- [Usage Guide](https://github.com/wshobson/agents/blob/main/docs/usage.md)
+  Why it matters: authoritative reference on `Usage Guide` (github.com).
+- [Agent Reference](https://github.com/wshobson/agents/blob/main/docs/agents.md)
+  Why it matters: authoritative reference on `Agent Reference` (github.com).
+- [Agent Skills](https://github.com/wshobson/agents/blob/main/docs/agent-skills.md)
+  Why it matters: authoritative reference on `Agent Skills` (github.com).
+- [Architecture Guide](https://github.com/wshobson/agents/blob/main/docs/architecture.md)
+  Why it matters: authoritative reference on `Architecture Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Marketplace Architecture and Plugin Structure](02-marketplace-architecture-and-plugin-structure.md)
+- [Next Chapter: Chapter 4: Commands, Natural Language, and Workflow Orchestration](04-commands-natural-language-and-workflow-orchestration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/wshobson-agents-tutorial/04-commands-natural-language-and-workflow-orchestration.md b/tutorials/wshobson-agents-tutorial/04-commands-natural-language-and-workflow-orchestration.md
index 835699ae..bc8cf786 100644
--- a/tutorials/wshobson-agents-tutorial/04-commands-natural-language-and-workflow-orchestration.md
+++ b/tutorials/wshobson-agents-tutorial/04-commands-natural-language-and-workflow-orchestration.md
@@ -7,6 +7,9 @@ parent: Wshobson Agents Tutorial
 
 # Chapter 4: Commands, Natural Language, and Workflow Orchestration
 
+Welcome to **Chapter 4: Commands, Natural Language, and Workflow Orchestration**. In this part of **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers the two primary interfaces and when to use each.
 
 ## Learning Goals
@@ -58,3 +61,581 @@ Benefits:
 You now have a balanced command/NL operating model for reliable multi-agent workflows.
 
 Next: [Chapter 5: Agents, Skills, and Model Tier Strategy](05-agents-skills-and-model-tier-strategy.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- tutorial slug: **wshobson-agents-tutorial**
+- chapter focus: **Chapter 4: Commands, Natural Language, and Workflow Orchestration**
+- system context: **Wshobson Agents Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Commands, Natural Language, and Workflow Orchestration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Repository README](https://github.com/wshobson/agents/blob/main/README.md)
+- [Plugin Reference](https://github.com/wshobson/agents/blob/main/docs/plugins.md)
+- [Usage Guide](https://github.com/wshobson/agents/blob/main/docs/usage.md)
+- [Agent Reference](https://github.com/wshobson/agents/blob/main/docs/agents.md)
+- [Agent Skills](https://github.com/wshobson/agents/blob/main/docs/agent-skills.md)
+- [Architecture Guide](https://github.com/wshobson/agents/blob/main/docs/architecture.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [AGENTS.md Tutorial](../agents-md-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Commands, Natural Language, and Workflow Orchestration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Commands, Natural Language, and Workflow Orchestration
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `full`, `stack`, `security` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Commands, Natural Language, and Workflow Orchestration` as an operating subsystem inside **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `orchestration`, `feature`, `user` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Commands, Natural Language, and Workflow Orchestration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `full`.
+2. **Input normalization**: shape incoming data so `stack` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `security`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Repository README](https://github.com/wshobson/agents/blob/main/README.md)
+  Why it matters: authoritative reference on `Repository README` (github.com).
+- [Plugin Reference](https://github.com/wshobson/agents/blob/main/docs/plugins.md)
+  Why it matters: authoritative reference on `Plugin Reference` (github.com).
+- [Usage Guide](https://github.com/wshobson/agents/blob/main/docs/usage.md)
+  Why it matters: authoritative reference on `Usage Guide` (github.com).
+- [Agent Reference](https://github.com/wshobson/agents/blob/main/docs/agents.md)
+  Why it matters: authoritative reference on `Agent Reference` (github.com).
+- [Agent Skills](https://github.com/wshobson/agents/blob/main/docs/agent-skills.md)
+  Why it matters: authoritative reference on `Agent Skills` (github.com).
+- [Architecture Guide](https://github.com/wshobson/agents/blob/main/docs/architecture.md)
+  Why it matters: authoritative reference on `Architecture Guide` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `full` and `stack` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Installation and Plugin Selection Strategy](03-installation-and-plugin-selection-strategy.md)
+- [Next Chapter: Chapter 5: Agents, Skills, and Model Tier Strategy](05-agents-skills-and-model-tier-strategy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/wshobson-agents-tutorial/05-agents-skills-and-model-tier-strategy.md b/tutorials/wshobson-agents-tutorial/05-agents-skills-and-model-tier-strategy.md
index fe152f83..0e31bd0d 100644
--- a/tutorials/wshobson-agents-tutorial/05-agents-skills-and-model-tier-strategy.md
+++ b/tutorials/wshobson-agents-tutorial/05-agents-skills-and-model-tier-strategy.md
@@ -7,6 +7,9 @@ parent: Wshobson Agents Tutorial
 
 # Chapter 5: Agents, Skills, and Model Tier Strategy
 
+Welcome to **Chapter 5: Agents, Skills, and Model Tier Strategy**. In this part of **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains how specialists, skill packs, and model assignment combine to shape output quality and cost.
 
 ## Learning Goals
@@ -50,3 +53,589 @@ Practical heuristic:
 You now understand how to combine specialists, skills, and model strategy for better outcomes.
 
 Next: [Chapter 6: Multi-Agent Team Patterns and Production Workflows](06-multi-agent-team-patterns-and-production-workflows.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- tutorial slug: **wshobson-agents-tutorial**
+- chapter focus: **Chapter 5: Agents, Skills, and Model Tier Strategy**
+- system context: **Wshobson Agents Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Agents, Skills, and Model Tier Strategy`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Repository README](https://github.com/wshobson/agents/blob/main/README.md)
+- [Plugin Reference](https://github.com/wshobson/agents/blob/main/docs/plugins.md)
+- [Usage Guide](https://github.com/wshobson/agents/blob/main/docs/usage.md)
+- [Agent Reference](https://github.com/wshobson/agents/blob/main/docs/agents.md)
+- [Agent Skills](https://github.com/wshobson/agents/blob/main/docs/agent-skills.md)
+- [Architecture Guide](https://github.com/wshobson/agents/blob/main/docs/architecture.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [AGENTS.md Tutorial](../agents-md-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Agents, Skills, and Model Tier Strategy`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Agents, Skills, and Model Tier Strategy
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Agents, Skills, and Model Tier Strategy` as an operating subsystem inside **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Agents, Skills, and Model Tier Strategy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Repository README](https://github.com/wshobson/agents/blob/main/README.md)
+  Why it matters: authoritative reference on `Repository README` (github.com).
+- [Plugin Reference](https://github.com/wshobson/agents/blob/main/docs/plugins.md)
+  Why it matters: authoritative reference on `Plugin Reference` (github.com).
+- [Usage Guide](https://github.com/wshobson/agents/blob/main/docs/usage.md)
+  Why it matters: authoritative reference on `Usage Guide` (github.com).
+- [Agent Reference](https://github.com/wshobson/agents/blob/main/docs/agents.md)
+  Why it matters: authoritative reference on `Agent Reference` (github.com).
+- [Agent Skills](https://github.com/wshobson/agents/blob/main/docs/agent-skills.md)
+  Why it matters: authoritative reference on `Agent Skills` (github.com).
+- [Architecture Guide](https://github.com/wshobson/agents/blob/main/docs/architecture.md)
+  Why it matters: authoritative reference on `Architecture Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Commands, Natural Language, and Workflow Orchestration](04-commands-natural-language-and-workflow-orchestration.md)
+- [Next Chapter: Chapter 6: Multi-Agent Team Patterns and Production Workflows](06-multi-agent-team-patterns-and-production-workflows.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/wshobson-agents-tutorial/06-multi-agent-team-patterns-and-production-workflows.md b/tutorials/wshobson-agents-tutorial/06-multi-agent-team-patterns-and-production-workflows.md
index 641bbffd..0fa37abe 100644
--- a/tutorials/wshobson-agents-tutorial/06-multi-agent-team-patterns-and-production-workflows.md
+++ b/tutorials/wshobson-agents-tutorial/06-multi-agent-team-patterns-and-production-workflows.md
@@ -7,6 +7,9 @@ parent: Wshobson Agents Tutorial
 
 # Chapter 6: Multi-Agent Team Patterns and Production Workflows
 
+Welcome to **Chapter 6: Multi-Agent Team Patterns and Production Workflows**. In this part of **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on orchestrated workflows where multiple agents collaborate with clear handoffs.
 
 ## Learning Goals
@@ -54,3 +57,589 @@ This chapter focuses on orchestrated workflows where multiple agents collaborate
 You now have concrete patterns for reliable multi-agent collaboration.
 
 Next: [Chapter 7: Governance, Safety, and Operational Best Practices](07-governance-safety-and-operational-best-practices.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- tutorial slug: **wshobson-agents-tutorial**
+- chapter focus: **Chapter 6: Multi-Agent Team Patterns and Production Workflows**
+- system context: **Wshobson Agents Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Multi-Agent Team Patterns and Production Workflows`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Repository README](https://github.com/wshobson/agents/blob/main/README.md)
+- [Plugin Reference](https://github.com/wshobson/agents/blob/main/docs/plugins.md)
+- [Usage Guide](https://github.com/wshobson/agents/blob/main/docs/usage.md)
+- [Agent Reference](https://github.com/wshobson/agents/blob/main/docs/agents.md)
+- [Agent Skills](https://github.com/wshobson/agents/blob/main/docs/agent-skills.md)
+- [Architecture Guide](https://github.com/wshobson/agents/blob/main/docs/architecture.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [AGENTS.md Tutorial](../agents-md-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Multi-Agent Team Patterns and Production Workflows`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Multi-Agent Team Patterns and Production Workflows
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Multi-Agent Team Patterns and Production Workflows` as an operating subsystem inside **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Multi-Agent Team Patterns and Production Workflows` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Repository README](https://github.com/wshobson/agents/blob/main/README.md)
+  Why it matters: authoritative reference on `Repository README` (github.com).
+- [Plugin Reference](https://github.com/wshobson/agents/blob/main/docs/plugins.md)
+  Why it matters: authoritative reference on `Plugin Reference` (github.com).
+- [Usage Guide](https://github.com/wshobson/agents/blob/main/docs/usage.md)
+  Why it matters: authoritative reference on `Usage Guide` (github.com).
+- [Agent Reference](https://github.com/wshobson/agents/blob/main/docs/agents.md)
+  Why it matters: authoritative reference on `Agent Reference` (github.com).
+- [Agent Skills](https://github.com/wshobson/agents/blob/main/docs/agent-skills.md)
+  Why it matters: authoritative reference on `Agent Skills` (github.com).
+- [Architecture Guide](https://github.com/wshobson/agents/blob/main/docs/architecture.md)
+  Why it matters: authoritative reference on `Architecture Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Agents, Skills, and Model Tier Strategy](05-agents-skills-and-model-tier-strategy.md)
+- [Next Chapter: Chapter 7: Governance, Safety, and Operational Best Practices](07-governance-safety-and-operational-best-practices.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/wshobson-agents-tutorial/07-governance-safety-and-operational-best-practices.md b/tutorials/wshobson-agents-tutorial/07-governance-safety-and-operational-best-practices.md
index 5f57b354..e3fc50bb 100644
--- a/tutorials/wshobson-agents-tutorial/07-governance-safety-and-operational-best-practices.md
+++ b/tutorials/wshobson-agents-tutorial/07-governance-safety-and-operational-best-practices.md
@@ -7,6 +7,9 @@ parent: Wshobson Agents Tutorial
 
 # Chapter 7: Governance, Safety, and Operational Best Practices
 
+Welcome to **Chapter 7: Governance, Safety, and Operational Best Practices**. In this part of **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter establishes team-level controls so plugin scale does not become operational chaos.
 
 ## Learning Goals
@@ -48,3 +51,589 @@ This chapter establishes team-level controls so plugin scale does not become ope
 You now have a governance model for scaling plugin-based agent operations.
 
 Next: [Chapter 8: Contribution Workflow and Plugin Authoring Patterns](08-contribution-workflow-and-plugin-authoring-patterns.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- tutorial slug: **wshobson-agents-tutorial**
+- chapter focus: **Chapter 7: Governance, Safety, and Operational Best Practices**
+- system context: **Wshobson Agents Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Governance, Safety, and Operational Best Practices`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Repository README](https://github.com/wshobson/agents/blob/main/README.md)
+- [Plugin Reference](https://github.com/wshobson/agents/blob/main/docs/plugins.md)
+- [Usage Guide](https://github.com/wshobson/agents/blob/main/docs/usage.md)
+- [Agent Reference](https://github.com/wshobson/agents/blob/main/docs/agents.md)
+- [Agent Skills](https://github.com/wshobson/agents/blob/main/docs/agent-skills.md)
+- [Architecture Guide](https://github.com/wshobson/agents/blob/main/docs/architecture.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [AGENTS.md Tutorial](../agents-md-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Governance, Safety, and Operational Best Practices`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Governance, Safety, and Operational Best Practices
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Governance, Safety, and Operational Best Practices` as an operating subsystem inside **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Governance, Safety, and Operational Best Practices` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Repository README](https://github.com/wshobson/agents/blob/main/README.md)
+  Why it matters: authoritative reference on `Repository README` (github.com).
+- [Plugin Reference](https://github.com/wshobson/agents/blob/main/docs/plugins.md)
+  Why it matters: authoritative reference on `Plugin Reference` (github.com).
+- [Usage Guide](https://github.com/wshobson/agents/blob/main/docs/usage.md)
+  Why it matters: authoritative reference on `Usage Guide` (github.com).
+- [Agent Reference](https://github.com/wshobson/agents/blob/main/docs/agents.md)
+  Why it matters: authoritative reference on `Agent Reference` (github.com).
+- [Agent Skills](https://github.com/wshobson/agents/blob/main/docs/agent-skills.md)
+  Why it matters: authoritative reference on `Agent Skills` (github.com).
+- [Architecture Guide](https://github.com/wshobson/agents/blob/main/docs/architecture.md)
+  Why it matters: authoritative reference on `Architecture Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Multi-Agent Team Patterns and Production Workflows](06-multi-agent-team-patterns-and-production-workflows.md)
+- [Next Chapter: Chapter 8: Contribution Workflow and Plugin Authoring Patterns](08-contribution-workflow-and-plugin-authoring-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/wshobson-agents-tutorial/08-contribution-workflow-and-plugin-authoring-patterns.md b/tutorials/wshobson-agents-tutorial/08-contribution-workflow-and-plugin-authoring-patterns.md
index 4bd02b71..44e30b84 100644
--- a/tutorials/wshobson-agents-tutorial/08-contribution-workflow-and-plugin-authoring-patterns.md
+++ b/tutorials/wshobson-agents-tutorial/08-contribution-workflow-and-plugin-authoring-patterns.md
@@ -7,6 +7,9 @@ parent: Wshobson Agents Tutorial
 
 # Chapter 8: Contribution Workflow and Plugin Authoring Patterns
 
+Welcome to **Chapter 8: Contribution Workflow and Plugin Authoring Patterns**. In this part of **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter provides a practical path for submitting high-quality plugin and documentation contributions.
 
 ## Learning Goals
@@ -53,3 +56,588 @@ Next steps:
 - curate your team's approved plugin baseline
 - codify command templates for repeatable workflows
 - contribute one focused plugin or documentation improvement
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- tutorial slug: **wshobson-agents-tutorial**
+- chapter focus: **Chapter 8: Contribution Workflow and Plugin Authoring Patterns**
+- system context: **Wshobson Agents Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Contribution Workflow and Plugin Authoring Patterns`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Repository README](https://github.com/wshobson/agents/blob/main/README.md)
+- [Plugin Reference](https://github.com/wshobson/agents/blob/main/docs/plugins.md)
+- [Usage Guide](https://github.com/wshobson/agents/blob/main/docs/usage.md)
+- [Agent Reference](https://github.com/wshobson/agents/blob/main/docs/agents.md)
+- [Agent Skills](https://github.com/wshobson/agents/blob/main/docs/agent-skills.md)
+- [Architecture Guide](https://github.com/wshobson/agents/blob/main/docs/architecture.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [AGENTS.md Tutorial](../agents-md-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Contribution Workflow and Plugin Authoring Patterns`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Contribution Workflow and Plugin Authoring Patterns
+
+- tutorial context: **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Contribution Workflow and Plugin Authoring Patterns` as an operating subsystem inside **Wshobson Agents Tutorial: Pluginized Multi-Agent Workflows for Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Contribution Workflow and Plugin Authoring Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Repository README](https://github.com/wshobson/agents/blob/main/README.md)
+  Why it matters: authoritative reference on `Repository README` (github.com).
+- [Plugin Reference](https://github.com/wshobson/agents/blob/main/docs/plugins.md)
+  Why it matters: authoritative reference on `Plugin Reference` (github.com).
+- [Usage Guide](https://github.com/wshobson/agents/blob/main/docs/usage.md)
+  Why it matters: authoritative reference on `Usage Guide` (github.com).
+- [Agent Reference](https://github.com/wshobson/agents/blob/main/docs/agents.md)
+  Why it matters: authoritative reference on `Agent Reference` (github.com).
+- [Agent Skills](https://github.com/wshobson/agents/blob/main/docs/agent-skills.md)
+  Why it matters: authoritative reference on `Agent Skills` (github.com).
+- [Architecture Guide](https://github.com/wshobson/agents/blob/main/docs/architecture.md)
+  Why it matters: authoritative reference on `Architecture Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Governance, Safety, and Operational Best Practices](07-governance-safety-and-operational-best-practices.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)